Overview

Dataset statistics

Number of variables115
Number of observations12760
Missing cells178687
Missing cells (%)12.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.2 MiB
Average record size in memory920.0 B

Variable types

Categorical23
Numeric92

Warnings

session has constant value "2020" Constant
cod_uai has a high cardinality: 3753 distinct values High cardinality
g_ea_lib_vx has a high cardinality: 3395 distinct values High cardinality
dep has a high cardinality: 103 distinct values High cardinality
dep_lib has a high cardinality: 103 distinct values High cardinality
lib_comp_voe_ins has a high cardinality: 11125 distinct values High cardinality
regr_forma has a high cardinality: 53 distinct values High cardinality
fil_lib_voe_acc has a high cardinality: 517 distinct values High cardinality
detail_forma has a high cardinality: 2410 distinct values High cardinality
lien_form_psup has a high cardinality: 12580 distinct values High cardinality
g_olocalisation_des_formations has a high cardinality: 4930 distinct values High cardinality
regr_forma has 12247 (96.0%) missing values Missing
detail_forma has 9361 (73.4%) missing values Missing
lien_form_psup has 180 (1.4%) missing values Missing
g_olocalisation_des_formations has 179 (1.4%) missing values Missing
nb_voe_pp_internat has 11913 (93.4%) missing values Missing
nb_cla_pp_internat has 11913 (93.4%) missing values Missing
nb_cla_pp_pasinternat has 11913 (93.4%) missing values Missing
acc_internat has 11913 (93.4%) missing values Missing
acc_term has 6740 (52.8%) missing values Missing
acc_term_f has 6740 (52.8%) missing values Missing
pct_aca_orig has 159 (1.2%) missing values Missing
pct_aca_orig_idf has 159 (1.2%) missing values Missing
pct_etab_orig has 6748 (52.9%) missing values Missing
pct_bours has 159 (1.2%) missing values Missing
pct_mention_nonrenseignee has 159 (1.2%) missing values Missing
pct_sansmention has 159 (1.2%) missing values Missing
pct_ab has 159 (1.2%) missing values Missing
pct_b has 159 (1.2%) missing values Missing
pct_tb has 159 (1.2%) missing values Missing
pct_bg has 159 (1.2%) missing values Missing
pct_bg_mention has 159 (1.2%) missing values Missing
pct_bt has 159 (1.2%) missing values Missing
pct_bt_mention has 159 (1.2%) missing values Missing
pct_bp has 159 (1.2%) missing values Missing
pct_bp_mention has 159 (1.2%) missing values Missing
lib_grp1 has 234 (1.8%) missing values Missing
ran_grp1 has 234 (1.8%) missing values Missing
lib_grp2 has 6795 (53.3%) missing values Missing
ran_grp2 has 6795 (53.3%) missing values Missing
lib_grp3 has 8913 (69.9%) missing values Missing
ran_grp3 has 8913 (69.9%) missing values Missing
lib_grp4 has 12746 (99.9%) missing values Missing
ran_grp4 has 12746 (99.9%) missing values Missing
lib_grp5 has 12746 (99.9%) missing values Missing
ran_grp5 has 12746 (99.9%) missing values Missing
taux_adm_psup has 234 (1.8%) missing values Missing
taux_adm_psup_pro has 1062 (8.3%) missing values Missing
taux_adm_psup_gen has 1062 (8.3%) missing values Missing
taux_adm_psup_techno has 1062 (8.3%) missing values Missing
ran_grp2 is highly skewed (γ1 = 26.16505334) Skewed
ran_grp3 is highly skewed (γ1 = 35.5607139) Skewed
lien_form_psup is uniformly distributed Uniform
cod_aff_form has unique values Unique
voe_tot has 195 (1.5%) zeros Zeros
voe_tot_f has 262 (2.1%) zeros Zeros
nb_voe_pp has 227 (1.8%) zeros Zeros
nb_voe_pp_internat has 196 (1.5%) zeros Zeros
nb_voe_pp_bg has 715 (5.6%) zeros Zeros
nb_voe_pp_bg_brs has 1198 (9.4%) zeros Zeros
nb_voe_pp_bt has 960 (7.5%) zeros Zeros
nb_voe_pp_bt_brs has 1618 (12.7%) zeros Zeros
nb_voe_pp_bp has 1799 (14.1%) zeros Zeros
nb_voe_pp_bp_brs has 2502 (19.6%) zeros Zeros
nb_voe_pp_at has 310 (2.4%) zeros Zeros
nb_voe_pc has 6284 (49.2%) zeros Zeros
nb_voe_pc_bg has 8338 (65.3%) zeros Zeros
nb_voe_pc_bt has 8290 (65.0%) zeros Zeros
nb_voe_pc_bp has 8627 (67.6%) zeros Zeros
nb_voe_pc_at has 6614 (51.8%) zeros Zeros
nb_cla_pp has 234 (1.8%) zeros Zeros
nb_cla_pc has 7073 (55.4%) zeros Zeros
nb_cla_pp_internat has 198 (1.6%) zeros Zeros
nb_cla_pp_bg has 765 (6.0%) zeros Zeros
nb_cla_pp_bg_brs has 1505 (11.8%) zeros Zeros
nb_cla_pp_bt has 1691 (13.3%) zeros Zeros
nb_cla_pp_bt_brs has 2325 (18.2%) zeros Zeros
nb_cla_pp_bp has 2898 (22.7%) zeros Zeros
nb_cla_pp_bp_brs has 3631 (28.5%) zeros Zeros
nb_cla_pp_at has 508 (4.0%) zeros Zeros
acc_tot_f has 1063 (8.3%) zeros Zeros
acc_pp has 312 (2.4%) zeros Zeros
acc_pc has 7669 (60.1%) zeros Zeros
acc_debutpp has 1104 (8.7%) zeros Zeros
acc_datebac has 336 (2.6%) zeros Zeros
acc_finpp has 188 (1.5%) zeros Zeros
acc_internat has 210 (1.6%) zeros Zeros
acc_brs has 976 (7.6%) zeros Zeros
acc_neobac has 159 (1.2%) zeros Zeros
acc_bg has 1673 (13.1%) zeros Zeros
acc_bt has 2601 (20.4%) zeros Zeros
acc_bp has 4316 (33.8%) zeros Zeros
acc_at has 1405 (11.0%) zeros Zeros
acc_mention_nonrenseignee has 11375 (89.1%) zeros Zeros
acc_sansmention has 2083 (16.3%) zeros Zeros
acc_ab has 1023 (8.0%) zeros Zeros
acc_b has 1419 (11.1%) zeros Zeros
acc_tb has 4708 (36.9%) zeros Zeros
acc_bg_mention has 3184 (25.0%) zeros Zeros
acc_bt_mention has 3569 (28.0%) zeros Zeros
acc_bp_mention has 4761 (37.3%) zeros Zeros
acc_term has 962 (7.5%) zeros Zeros
acc_term_f has 2739 (21.5%) zeros Zeros
acc_aca_orig has 363 (2.8%) zeros Zeros
acc_aca_orig_idf has 269 (2.1%) zeros Zeros
pct_acc_debutpp has 1039 (8.1%) zeros Zeros
pct_acc_datebac has 271 (2.1%) zeros Zeros
pct_f has 998 (7.8%) zeros Zeros
pct_aca_orig has 204 (1.6%) zeros Zeros
pct_etab_orig has 954 (7.5%) zeros Zeros
pct_bours has 817 (6.4%) zeros Zeros
pct_mention_nonrenseignee has 11216 (87.9%) zeros Zeros
pct_sansmention has 1924 (15.1%) zeros Zeros
pct_ab has 864 (6.8%) zeros Zeros
pct_b has 1260 (9.9%) zeros Zeros
pct_tb has 4549 (35.7%) zeros Zeros
pct_bg has 1514 (11.9%) zeros Zeros
pct_bg_mention has 3025 (23.7%) zeros Zeros
pct_bt has 2442 (19.1%) zeros Zeros
pct_bt_mention has 3410 (26.7%) zeros Zeros
pct_bp has 4157 (32.6%) zeros Zeros
pct_bp_mention has 4602 (36.1%) zeros Zeros
prop_tot_bg has 741 (5.8%) zeros Zeros
prop_tot_bg_brs has 1631 (12.8%) zeros Zeros
prop_tot_bt has 1768 (13.9%) zeros Zeros
prop_tot_bt_brs has 2514 (19.7%) zeros Zeros
prop_tot_bp has 3269 (25.6%) zeros Zeros
prop_tot_bp_brs has 4017 (31.5%) zeros Zeros
prop_tot_at has 467 (3.7%) zeros Zeros
taux_adm_psup_pro has 3305 (25.9%) zeros Zeros
taux_adm_psup_gen has 762 (6.0%) zeros Zeros
taux_adm_psup_techno has 1788 (14.0%) zeros Zeros

Reproduction

Analysis started2021-03-18 15:43:21.081228
Analysis finished2021-03-18 15:43:33.461110
Duration12.38 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

session
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.8 KiB
2020
12760 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters51040
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020
ValueCountFrequency (%)
202012760
100.0%
2021-03-18T16:43:33.628402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-18T16:43:33.802717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
202012760
100.0%

Most occurring characters

ValueCountFrequency (%)
225520
50.0%
025520
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number51040
100.0%

Most frequent character per category

ValueCountFrequency (%)
225520
50.0%
025520
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common51040
100.0%

Most frequent character per script

ValueCountFrequency (%)
225520
50.0%
025520
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII51040
100.0%

Most frequent character per block

ValueCountFrequency (%)
225520
50.0%
025520
50.0%

contrat_etab
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.8 KiB
Public
10171 
Privé sous contrat d'association
1990 
Privé enseignement supérieur
 
531
Privé hors contrat
 
68

Length

Max length32
Median length6
Mean length11.03432602
Min length6

Characters and Unicode

Total characters140798
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPublic
2nd rowPublic
3rd rowPublic
4th rowPublic
5th rowPublic
ValueCountFrequency (%)
Public10171
79.7%
Privé sous contrat d'association1990
 
15.6%
Privé enseignement supérieur531
 
4.2%
Privé hors contrat68
 
0.5%
2021-03-18T16:43:33.955151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-18T16:43:34.007969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
public10171
51.0%
privé2589
 
13.0%
contrat2058
 
10.3%
sous1990
 
10.0%
d'association1990
 
10.0%
supérieur531
 
2.7%
enseignement531
 
2.7%
hors68
 
0.3%

Most occurring characters

ValueCountFrequency (%)
i17802
12.6%
c14219
10.1%
u13223
9.4%
P12760
9.1%
b10171
 
7.2%
l10171
 
7.2%
s9090
 
6.5%
o8096
 
5.8%
7168
 
5.1%
t6637
 
4.7%
Other values (12)31461
22.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter118880
84.4%
Uppercase Letter12760
 
9.1%
Space Separator7168
 
5.1%
Other Punctuation1990
 
1.4%

Most frequent character per category

ValueCountFrequency (%)
i17802
15.0%
c14219
12.0%
u13223
11.1%
b10171
8.6%
l10171
8.6%
s9090
7.6%
o8096
6.8%
t6637
 
5.6%
a6038
 
5.1%
r5777
 
4.9%
Other values (9)17656
14.9%
ValueCountFrequency (%)
P12760
100.0%
ValueCountFrequency (%)
7168
100.0%
ValueCountFrequency (%)
'1990
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin131640
93.5%
Common9158
 
6.5%

Most frequent character per script

ValueCountFrequency (%)
i17802
13.5%
c14219
10.8%
u13223
10.0%
P12760
9.7%
b10171
7.7%
l10171
7.7%
s9090
 
6.9%
o8096
 
6.2%
t6637
 
5.0%
a6038
 
4.6%
Other values (10)23433
17.8%
ValueCountFrequency (%)
7168
78.3%
'1990
 
21.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII137678
97.8%
None3120
 
2.2%

Most frequent character per block

ValueCountFrequency (%)
i17802
12.9%
c14219
10.3%
u13223
9.6%
P12760
9.3%
b10171
 
7.4%
l10171
 
7.4%
s9090
 
6.6%
o8096
 
5.9%
7168
 
5.2%
t6637
 
4.8%
Other values (11)28341
20.6%
ValueCountFrequency (%)
é3120
100.0%

cod_uai
Categorical

HIGH CARDINALITY

Distinct3753
Distinct (%)29.4%
Missing0
Missing (%)0.0%
Memory size99.8 KiB
0597065J
 
118
0673021V
 
90
0751719L
 
84
0921204J
 
83
0753488J
 
76
Other values (3748)
12309 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters102080
Distinct characters35
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1617 ?
Unique (%)12.7%

Sample

1st row0754902W
2nd row0942070P
3rd row0161051F
4th row0421574H
5th row0090559J
ValueCountFrequency (%)
0597065J118
 
0.9%
0673021V90
 
0.7%
0751719L84
 
0.7%
0921204J83
 
0.7%
0753488J76
 
0.6%
0755976N76
 
0.6%
0860856N75
 
0.6%
0751720M74
 
0.6%
0632035V71
 
0.6%
0134017W71
 
0.6%
Other values (3743)11942
93.6%
2021-03-18T16:43:34.192637image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0597065j118
 
0.9%
0673021v90
 
0.7%
0751719l84
 
0.7%
0921204j83
 
0.7%
0755976n76
 
0.6%
0753488j76
 
0.6%
0860856n75
 
0.6%
0751720m74
 
0.6%
0632035v71
 
0.6%
0134017w71
 
0.6%
Other values (3743)11942
93.6%

Most occurring characters

ValueCountFrequency (%)
026181
25.6%
110036
 
9.8%
37588
 
7.4%
27358
 
7.2%
77208
 
7.1%
96828
 
6.7%
56507
 
6.4%
46259
 
6.1%
66104
 
6.0%
85245
 
5.1%
Other values (25)12766
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number89314
87.5%
Uppercase Letter12766
 
12.5%

Most frequent character per category

ValueCountFrequency (%)
J781
 
6.1%
N666
 
5.2%
F649
 
5.1%
E623
 
4.9%
M601
 
4.7%
R598
 
4.7%
L589
 
4.6%
G586
 
4.6%
U585
 
4.6%
V579
 
4.5%
Other values (15)6509
51.0%
ValueCountFrequency (%)
026181
29.3%
110036
 
11.2%
37588
 
8.5%
27358
 
8.2%
77208
 
8.1%
96828
 
7.6%
56507
 
7.3%
46259
 
7.0%
66104
 
6.8%
85245
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
Common89314
87.5%
Latin12766
 
12.5%

Most frequent character per script

ValueCountFrequency (%)
J781
 
6.1%
N666
 
5.2%
F649
 
5.1%
E623
 
4.9%
M601
 
4.7%
R598
 
4.7%
L589
 
4.6%
G586
 
4.6%
U585
 
4.6%
V579
 
4.5%
Other values (15)6509
51.0%
ValueCountFrequency (%)
026181
29.3%
110036
 
11.2%
37588
 
8.5%
27358
 
8.2%
77208
 
8.1%
96828
 
7.6%
56507
 
7.3%
46259
 
7.0%
66104
 
6.8%
85245
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII102080
100.0%

Most frequent character per block

ValueCountFrequency (%)
026181
25.6%
110036
 
9.8%
37588
 
7.4%
27358
 
7.2%
77208
 
7.1%
96828
 
6.7%
56507
 
6.4%
46259
 
6.1%
66104
 
6.0%
85245
 
5.1%
Other values (25)12766
12.5%

g_ea_lib_vx
Categorical

HIGH CARDINALITY

Distinct3395
Distinct (%)26.6%
Missing0
Missing (%)0.0%
Memory size99.8 KiB
Université de Lille
 
118
Université de Strasbourg
 
90
Université Sorbonne Nouvelle Paris 3
 
84
Université Paris Nanterre
 
83
Université de Paris
 
76
Other values (3390)
12309 

Length

Max length115
Median length23
Mean length27.68401254
Min length3

Characters and Unicode

Total characters353248
Distinct characters83
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1421 ?
Unique (%)11.1%

Sample

1st rowIFSI Virginie Olivier CH Sainte Anne
2nd rowIFSI Jean Baptiste Pussin
3rd rowIFSI - Croix-Rouge Française - Angoulême
4th rowIFSI St Chamond
5th rowIFSI CH Int Val Ariège
ValueCountFrequency (%)
Université de Lille118
 
0.9%
Université de Strasbourg90
 
0.7%
Université Sorbonne Nouvelle Paris 384
 
0.7%
Université Paris Nanterre83
 
0.7%
Université de Paris76
 
0.6%
INALCO76
 
0.6%
Université de Poitiers75
 
0.6%
Sorbonne Université - Lettres, Arts, Langues, Sciences Humaines et Sociales74
 
0.6%
Université Clermont Auvergne71
 
0.6%
Aix Marseille Université - Site d'Aix-en-Provence71
 
0.6%
Other values (3385)11942
93.6%
2021-03-18T16:43:34.413411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lycée6422
 
12.1%
de4961
 
9.4%
université3275
 
6.2%
2239
 
4.2%
site744
 
1.4%
paris682
 
1.3%
i.u.t668
 
1.3%
la643
 
1.2%
professionnel566
 
1.1%
jean563
 
1.1%
Other values (3350)32198
60.8%

Most occurring characters

ValueCountFrequency (%)
e41295
 
11.7%
40207
 
11.4%
i22163
 
6.3%
n19235
 
5.4%
r18427
 
5.2%
a15976
 
4.5%
s15033
 
4.3%
t14715
 
4.2%
o13036
 
3.7%
é11860
 
3.4%
Other values (73)141301
40.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter241053
68.2%
Uppercase Letter62186
 
17.6%
Space Separator40207
 
11.4%
Dash Punctuation4484
 
1.3%
Other Punctuation3719
 
1.1%
Decimal Number902
 
0.3%
Open Punctuation348
 
0.1%
Close Punctuation348
 
0.1%
Connector Punctuation1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
e41295
17.1%
i22163
 
9.2%
n19235
 
8.0%
r18427
 
7.6%
a15976
 
6.6%
s15033
 
6.2%
t14715
 
6.1%
o13036
 
5.4%
é11860
 
4.9%
l11343
 
4.7%
Other values (26)57970
24.0%
ValueCountFrequency (%)
L10163
16.3%
S5220
 
8.4%
U5088
 
8.2%
E4058
 
6.5%
C4056
 
6.5%
A3925
 
6.3%
I3710
 
6.0%
P3495
 
5.6%
M2841
 
4.6%
T2648
 
4.3%
Other values (19)16982
27.3%
ValueCountFrequency (%)
3323
35.8%
1285
31.6%
2223
24.7%
857
 
6.3%
49
 
1.0%
64
 
0.4%
71
 
0.1%
ValueCountFrequency (%)
.2027
54.5%
'1258
33.8%
,382
 
10.3%
/45
 
1.2%
&6
 
0.2%
:1
 
< 0.1%
ValueCountFrequency (%)
40207
100.0%
ValueCountFrequency (%)
-4484
100.0%
ValueCountFrequency (%)
(348
100.0%
ValueCountFrequency (%)
)348
100.0%
ValueCountFrequency (%)
_1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin303239
85.8%
Common50009
 
14.2%

Most frequent character per script

ValueCountFrequency (%)
e41295
 
13.6%
i22163
 
7.3%
n19235
 
6.3%
r18427
 
6.1%
a15976
 
5.3%
s15033
 
5.0%
t14715
 
4.9%
o13036
 
4.3%
é11860
 
3.9%
l11343
 
3.7%
Other values (55)120156
39.6%
ValueCountFrequency (%)
40207
80.4%
-4484
 
9.0%
.2027
 
4.1%
'1258
 
2.5%
,382
 
0.8%
(348
 
0.7%
)348
 
0.7%
3323
 
0.6%
1285
 
0.6%
2223
 
0.4%
Other values (8)124
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII340464
96.4%
None12784
 
3.6%

Most frequent character per block

ValueCountFrequency (%)
e41295
 
12.1%
40207
 
11.8%
i22163
 
6.5%
n19235
 
5.6%
r18427
 
5.4%
a15976
 
4.7%
s15033
 
4.4%
t14715
 
4.3%
o13036
 
3.8%
l11343
 
3.3%
Other values (60)129034
37.9%
ValueCountFrequency (%)
é11860
92.8%
è313
 
2.4%
ô273
 
2.1%
ç186
 
1.5%
î35
 
0.3%
â30
 
0.2%
É27
 
0.2%
à19
 
0.1%
ê18
 
0.1%
ë18
 
0.1%
Other values (3)5
 
< 0.1%

dep
Categorical

HIGH CARDINALITY

Distinct103
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size99.8 KiB
75
 
898
59
 
663
69
 
449
13
 
409
31
 
352
Other values (98)
9989 

Length

Max length3
Median length2
Mean length2.045768025
Min length2

Characters and Unicode

Total characters26104
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row75
2nd row94
3rd row16
4th row42
5th row09
ValueCountFrequency (%)
75898
 
7.0%
59663
 
5.2%
69449
 
3.5%
13409
 
3.2%
31352
 
2.8%
33290
 
2.3%
93287
 
2.2%
92260
 
2.0%
44259
 
2.0%
62252
 
2.0%
Other values (93)8641
67.7%
2021-03-18T16:43:34.626068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
75898
 
7.0%
59663
 
5.2%
69449
 
3.5%
13409
 
3.2%
31352
 
2.8%
33290
 
2.3%
93287
 
2.2%
92260
 
2.0%
44259
 
2.0%
62252
 
2.0%
Other values (93)8641
67.7%

Most occurring characters

ValueCountFrequency (%)
73613
13.8%
93514
13.5%
53301
12.6%
33266
12.5%
62657
10.2%
42575
9.9%
12255
8.6%
22080
8.0%
81709
6.5%
01075
 
4.1%
Other values (2)59
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number26045
99.8%
Uppercase Letter59
 
0.2%

Most frequent character per category

ValueCountFrequency (%)
73613
13.9%
93514
13.5%
53301
12.7%
33266
12.5%
62657
10.2%
42575
9.9%
12255
8.7%
22080
8.0%
81709
6.6%
01075
 
4.1%
ValueCountFrequency (%)
B44
74.6%
A15
 
25.4%

Most occurring scripts

ValueCountFrequency (%)
Common26045
99.8%
Latin59
 
0.2%

Most frequent character per script

ValueCountFrequency (%)
73613
13.9%
93514
13.5%
53301
12.7%
33266
12.5%
62657
10.2%
42575
9.9%
12255
8.7%
22080
8.0%
81709
6.6%
01075
 
4.1%
ValueCountFrequency (%)
B44
74.6%
A15
 
25.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII26104
100.0%

Most frequent character per block

ValueCountFrequency (%)
73613
13.8%
93514
13.5%
53301
12.6%
33266
12.5%
62657
10.2%
42575
9.9%
12255
8.6%
22080
8.0%
81709
6.5%
01075
 
4.1%
Other values (2)59
 
0.2%

dep_lib
Categorical

HIGH CARDINALITY

Distinct103
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size99.8 KiB
Paris
 
898
Nord
 
663
Rhône
 
449
Bouches-du-Rhône
 
409
Haute-Garonne
 
352
Other values (98)
9989 

Length

Max length23
Median length8
Mean length9.337774295
Min length3

Characters and Unicode

Total characters119150
Distinct characters50
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParis
2nd rowVal-de-Marne
3rd rowCharente
4th rowLoire
5th rowAriège
ValueCountFrequency (%)
Paris898
 
7.0%
Nord663
 
5.2%
Rhône449
 
3.5%
Bouches-du-Rhône409
 
3.2%
Haute-Garonne352
 
2.8%
Gironde290
 
2.3%
Seine-Saint-Denis287
 
2.2%
Hauts-de-Seine260
 
2.0%
Loire-Atlantique259
 
2.0%
Pas-de-Calais252
 
2.0%
Other values (93)8641
67.7%
2021-03-18T16:43:34.829454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
paris898
 
6.8%
nord663
 
5.1%
rhône449
 
3.4%
bouches-du-rhône409
 
3.1%
haute-garonne352
 
2.7%
gironde290
 
2.2%
seine-saint-denis287
 
2.2%
hauts-de-seine260
 
2.0%
loire-atlantique259
 
2.0%
pas-de-calais252
 
1.9%
Other values (99)9003
68.6%

Most occurring characters

ValueCountFrequency (%)
e17645
14.8%
n9061
 
7.6%
i9004
 
7.6%
a8859
 
7.4%
-8240
 
6.9%
r8125
 
6.8%
s6175
 
5.2%
t5574
 
4.7%
o5485
 
4.6%
u4142
 
3.5%
Other values (40)36840
30.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter91835
77.1%
Uppercase Letter18281
 
15.3%
Dash Punctuation8240
 
6.9%
Other Punctuation432
 
0.4%
Space Separator362
 
0.3%

Most frequent character per category

ValueCountFrequency (%)
e17645
19.2%
n9061
9.9%
i9004
9.8%
a8859
9.6%
r8125
8.8%
s6175
 
6.7%
t5574
 
6.1%
o5485
 
6.0%
u4142
 
4.5%
l3781
 
4.1%
Other values (17)13984
15.2%
ValueCountFrequency (%)
M2332
12.8%
S1875
10.3%
P1660
9.1%
R1418
 
7.8%
L1400
 
7.7%
H1331
 
7.3%
V1271
 
7.0%
A1048
 
5.7%
C1047
 
5.7%
G1026
 
5.6%
Other values (10)3873
21.2%
ValueCountFrequency (%)
-8240
100.0%
ValueCountFrequency (%)
'432
100.0%
ValueCountFrequency (%)
362
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin110116
92.4%
Common9034
 
7.6%

Most frequent character per script

ValueCountFrequency (%)
e17645
16.0%
n9061
 
8.2%
i9004
 
8.2%
a8859
 
8.0%
r8125
 
7.4%
s6175
 
5.6%
t5574
 
5.1%
o5485
 
5.0%
u4142
 
3.8%
l3781
 
3.4%
Other values (37)32265
29.3%
ValueCountFrequency (%)
-8240
91.2%
'432
 
4.8%
362
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII115877
97.3%
None3273
 
2.7%

Most frequent character per block

ValueCountFrequency (%)
e17645
15.2%
n9061
 
7.8%
i9004
 
7.8%
a8859
 
7.6%
-8240
 
7.1%
r8125
 
7.0%
s6175
 
5.3%
t5574
 
4.8%
o5485
 
4.7%
u4142
 
3.6%
Other values (36)33567
29.0%
ValueCountFrequency (%)
ô1479
45.2%
é1079
33.0%
è651
19.9%
ç64
 
2.0%

region_etab_aff
Categorical

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size99.8 KiB
Ile-de-France
2429 
Auvergne-Rhône-Alpes
1480 
Hauts-de-France
1225 
Occitanie
1078 
Nouvelle-Aquitaine
1069 
Other values (15)
5479 

Length

Max length26
Median length13
Mean length14.7815047
Min length5

Characters and Unicode

Total characters188612
Distinct characters42
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIle-de-France
2nd rowIle-de-France
3rd rowNouvelle-Aquitaine
4th rowAuvergne-Rhône-Alpes
5th rowOccitanie
ValueCountFrequency (%)
Ile-de-France2429
19.0%
Auvergne-Rhône-Alpes1480
11.6%
Hauts-de-France1225
9.6%
Occitanie1078
8.4%
Nouvelle-Aquitaine1069
8.4%
Grand Est1060
8.3%
Provence Alpes Côte d'Azur833
 
6.5%
Pays de la Loire740
 
5.8%
Bretagne685
 
5.4%
Normandie565
 
4.4%
Other values (10)1596
12.5%
2021-03-18T16:43:35.013688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ile-de-france2429
 
12.4%
auvergne-rhône-alpes1480
 
7.5%
hauts-de-france1225
 
6.2%
loire1143
 
5.8%
de1143
 
5.8%
occitanie1078
 
5.5%
nouvelle-aquitaine1069
 
5.5%
grand1060
 
5.4%
est1060
 
5.4%
la940
 
4.8%
Other values (18)6982
35.6%

Most occurring characters

ValueCountFrequency (%)
e30010
15.9%
n14094
 
7.5%
-12816
 
6.8%
a12417
 
6.6%
r11995
 
6.4%
l8201
 
4.3%
d7369
 
3.9%
c7181
 
3.8%
t7089
 
3.8%
6849
 
3.6%
Other values (32)70591
37.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter141290
74.9%
Uppercase Letter26824
 
14.2%
Dash Punctuation12816
 
6.8%
Space Separator6849
 
3.6%
Other Punctuation833
 
0.4%

Most frequent character per category

ValueCountFrequency (%)
e30010
21.2%
n14094
10.0%
a12417
8.8%
r11995
 
8.5%
l8201
 
5.8%
d7369
 
5.2%
c7181
 
5.1%
t7089
 
5.0%
u6813
 
4.8%
i6562
 
4.6%
Other values (14)29559
20.9%
ValueCountFrequency (%)
A5695
21.2%
F4192
15.6%
I2429
9.1%
C1833
 
6.8%
R1680
 
6.3%
P1637
 
6.1%
N1634
 
6.1%
L1343
 
5.0%
G1229
 
4.6%
H1225
 
4.6%
Other values (5)3927
14.6%
ValueCountFrequency (%)
-12816
100.0%
ValueCountFrequency (%)
6849
100.0%
ValueCountFrequency (%)
'833
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin168114
89.1%
Common20498
 
10.9%

Most frequent character per script

ValueCountFrequency (%)
e30010
17.9%
n14094
 
8.4%
a12417
 
7.4%
r11995
 
7.1%
l8201
 
4.9%
d7369
 
4.4%
c7181
 
4.3%
t7089
 
4.2%
u6813
 
4.1%
i6562
 
3.9%
Other values (29)56383
33.5%
ValueCountFrequency (%)
-12816
62.5%
6849
33.4%
'833
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII185433
98.3%
None3179
 
1.7%

Most frequent character per block

ValueCountFrequency (%)
e30010
16.2%
n14094
 
7.6%
-12816
 
6.9%
a12417
 
6.7%
r11995
 
6.5%
l8201
 
4.4%
d7369
 
4.0%
c7181
 
3.9%
t7089
 
3.8%
6849
 
3.7%
Other values (29)67412
36.4%
ValueCountFrequency (%)
ô2313
72.8%
é802
 
25.2%
ç64
 
2.0%

acad_mies
Categorical

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size99.8 KiB
Lille
915 
Paris
898 
Versailles
 
806
Nantes
 
740
Créteil
 
725
Other values (27)
8676 

Length

Max length19
Median length8
Mean length7.838322884
Min length4

Characters and Unicode

Total characters100017
Distinct characters41
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParis
2nd rowCréteil
3rd rowPoitiers
4th rowLyon
5th rowToulouse
ValueCountFrequency (%)
Lille915
 
7.2%
Paris898
 
7.0%
Versailles806
 
6.3%
Nantes740
 
5.8%
Créteil725
 
5.7%
Rennes685
 
5.4%
Lyon643
 
5.0%
Toulouse606
 
4.7%
Normandie565
 
4.4%
Bordeaux549
 
4.3%
Other values (22)5628
44.1%
2021-03-18T16:43:35.197075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lille915
 
7.0%
paris898
 
6.9%
versailles806
 
6.2%
nantes740
 
5.7%
créteil725
 
5.6%
rennes685
 
5.3%
lyon643
 
4.9%
toulouse606
 
4.7%
normandie565
 
4.3%
bordeaux549
 
4.2%
Other values (24)5892
45.2%

Most occurring characters

ValueCountFrequency (%)
e13442
13.4%
l8155
 
8.2%
r8099
 
8.1%
i8043
 
8.0%
s7755
 
7.8%
n7543
 
7.5%
o6571
 
6.6%
a6445
 
6.4%
t3555
 
3.6%
u3123
 
3.1%
Other values (31)27286
27.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter83467
83.5%
Uppercase Letter14655
 
14.7%
Dash Punctuation1631
 
1.6%
Space Separator264
 
0.3%

Most frequent character per category

ValueCountFrequency (%)
e13442
16.1%
l8155
9.8%
r8099
9.7%
i8043
9.6%
s7755
9.3%
n7543
9.0%
o6571
7.9%
a6445
7.7%
t3555
 
4.3%
u3123
 
3.7%
Other values (13)10736
12.9%
ValueCountFrequency (%)
N2043
13.9%
L1936
13.2%
M1548
10.6%
P1304
8.9%
R1170
8.0%
C1087
7.4%
T1009
6.9%
A820
 
5.6%
V806
 
5.5%
B785
 
5.4%
Other values (6)2147
14.7%
ValueCountFrequency (%)
-1631
100.0%
ValueCountFrequency (%)
264
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin98122
98.1%
Common1895
 
1.9%

Most frequent character per script

ValueCountFrequency (%)
e13442
13.7%
l8155
 
8.3%
r8099
 
8.3%
i8043
 
8.2%
s7755
 
7.9%
n7543
 
7.7%
o6571
 
6.7%
a6445
 
6.6%
t3555
 
3.6%
u3123
 
3.2%
Other values (29)25391
25.9%
ValueCountFrequency (%)
-1631
86.1%
264
 
13.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII98561
98.5%
None1456
 
1.5%

Most frequent character per block

ValueCountFrequency (%)
e13442
13.6%
l8155
 
8.3%
r8099
 
8.2%
i8043
 
8.2%
s7755
 
7.9%
n7543
 
7.7%
o6571
 
6.7%
a6445
 
6.5%
t3555
 
3.6%
u3123
 
3.2%
Other values (29)25830
26.2%
ValueCountFrequency (%)
é1392
95.6%
ç64
 
4.4%

select_form
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.8 KiB
formation selective
10035 
formation non selec
2725 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters242440
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowformation selective
2nd rowformation selective
3rd rowformation selective
4th rowformation selective
5th rowformation selective
ValueCountFrequency (%)
formation selective10035
78.6%
formation non selec2725
 
21.4%
2021-03-18T16:43:35.362486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-18T16:43:35.411739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
formation12760
45.2%
selective10035
35.5%
non2725
 
9.6%
selec2725
 
9.6%

Most occurring characters

ValueCountFrequency (%)
e35555
14.7%
o28245
11.7%
t22795
9.4%
i22795
9.4%
n18210
 
7.5%
15485
 
6.4%
f12760
 
5.3%
r12760
 
5.3%
m12760
 
5.3%
a12760
 
5.3%
Other values (4)48315
19.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter226955
93.6%
Space Separator15485
 
6.4%

Most frequent character per category

ValueCountFrequency (%)
e35555
15.7%
o28245
12.4%
t22795
10.0%
i22795
10.0%
n18210
8.0%
f12760
 
5.6%
r12760
 
5.6%
m12760
 
5.6%
a12760
 
5.6%
s12760
 
5.6%
Other values (3)35555
15.7%
ValueCountFrequency (%)
15485
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin226955
93.6%
Common15485
 
6.4%

Most frequent character per script

ValueCountFrequency (%)
e35555
15.7%
o28245
12.4%
t22795
10.0%
i22795
10.0%
n18210
8.0%
f12760
 
5.6%
r12760
 
5.6%
m12760
 
5.6%
a12760
 
5.6%
s12760
 
5.6%
Other values (3)35555
15.7%
ValueCountFrequency (%)
15485
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII242440
100.0%

Most frequent character per block

ValueCountFrequency (%)
e35555
14.7%
o28245
11.7%
t22795
9.4%
i22795
9.4%
n18210
 
7.5%
15485
 
6.4%
f12760
 
5.3%
r12760
 
5.3%
m12760
 
5.3%
a12760
 
5.3%
Other values (4)48315
19.9%

fili
Categorical

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size99.8 KiB
BTS
5173 
Licence
2578 
Autre formation
1578 
CPGE
847 
DUT
804 
Other values (6)
1780 

Length

Max length17
Median length4
Mean length6.305015674
Min length3

Characters and Unicode

Total characters80452
Distinct characters32
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIFSI
2nd rowIFSI
3rd rowIFSI
4th rowIFSI
5th rowIFSI
ValueCountFrequency (%)
BTS5173
40.5%
Licence2578
20.2%
Autre formation1578
 
12.4%
CPGE847
 
6.6%
DUT804
 
6.3%
Licence_Las457
 
3.6%
Ecole d'Ingénieur402
 
3.2%
IFSI329
 
2.6%
PASS227
 
1.8%
EFTS221
 
1.7%
2021-03-18T16:43:35.555298image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bts5173
34.4%
licence2578
17.2%
formation1578
 
10.5%
autre1578
 
10.5%
cpge847
 
5.6%
dut804
 
5.4%
ecole546
 
3.6%
licence_las457
 
3.0%
d'ingénieur402
 
2.7%
ifsi329
 
2.2%
Other values (4)736
 
4.9%

Most occurring characters

ValueCountFrequency (%)
e9028
 
11.2%
c6760
 
8.4%
T6198
 
7.7%
S6177
 
7.7%
n5417
 
6.7%
B5173
 
6.4%
i5015
 
6.2%
o3846
 
4.8%
r3702
 
4.6%
L3492
 
4.3%
Other values (22)25644
31.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter46736
58.1%
Uppercase Letter30589
38.0%
Space Separator2268
 
2.8%
Connector Punctuation457
 
0.6%
Other Punctuation402
 
0.5%

Most frequent character per category

ValueCountFrequency (%)
e9028
19.3%
c6760
14.5%
n5417
11.6%
i5015
10.7%
o3846
8.2%
r3702
7.9%
t3156
 
6.8%
a2035
 
4.4%
u1980
 
4.2%
m1866
 
4.0%
Other values (6)3931
8.4%
ValueCountFrequency (%)
T6198
20.3%
S6177
20.2%
B5173
16.9%
L3492
11.4%
A1805
 
5.9%
E1614
 
5.3%
P1074
 
3.5%
I1060
 
3.5%
C991
 
3.2%
G847
 
2.8%
Other values (3)2158
 
7.1%
ValueCountFrequency (%)
2268
100.0%
ValueCountFrequency (%)
'402
100.0%
ValueCountFrequency (%)
_457
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin77325
96.1%
Common3127
 
3.9%

Most frequent character per script

ValueCountFrequency (%)
e9028
11.7%
c6760
 
8.7%
T6198
 
8.0%
S6177
 
8.0%
n5417
 
7.0%
B5173
 
6.7%
i5015
 
6.5%
o3846
 
5.0%
r3702
 
4.8%
L3492
 
4.5%
Other values (19)22517
29.1%
ValueCountFrequency (%)
2268
72.5%
_457
 
14.6%
'402
 
12.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII80050
99.5%
None402
 
0.5%

Most frequent character per block

ValueCountFrequency (%)
e9028
 
11.3%
c6760
 
8.4%
T6198
 
7.7%
S6177
 
7.7%
n5417
 
6.8%
B5173
 
6.5%
i5015
 
6.3%
o3846
 
4.8%
r3702
 
4.6%
L3492
 
4.4%
Other values (21)25242
31.5%
ValueCountFrequency (%)
é402
100.0%

lib_comp_voe_ins
Categorical

HIGH CARDINALITY

Distinct11125
Distinct (%)87.2%
Missing0
Missing (%)0.0%
Memory size99.8 KiB
Concours Puissance Alpha - Formation d'ingénieur Bac + 5 - Bac S
 
49
Concours Geipi Polytech - Formation d'ingénieur Bac + 5 - Bac S
 
40
Concours SESAME - Formation des écoles de commerce et de management
 
36
Concours Puissance Alpha - Formation d'ingénieur Bac + 5 - Bac +1/+2
 
31
Concours Geipi Polytech - Formation d'ingénieur Bac + 5 - bac STI2D
 
28
Other values (11120)
12576 

Length

Max length307
Median length79
Mean length85.84161442
Min length23

Characters and Unicode

Total characters1095339
Distinct characters96
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10385 ?
Unique (%)81.4%

Sample

1st rowRegroupement d'IFSI Université Paris Descartes (P5) - D.E Infirmier
2nd rowRegroupement d'IFSI Université Paris Descartes (P5) - D.E Infirmier
3rd rowRegroupement d'IFSI Université Poitiers - D.E Infirmier
4th rowRegroupement d'IFSI Université St Etienne - D.E Infirmier
5th rowRegroupement d'IFSI Université Toulouse - D.E Infirmier
ValueCountFrequency (%)
Concours Puissance Alpha - Formation d'ingénieur Bac + 5 - Bac S49
 
0.4%
Concours Geipi Polytech - Formation d'ingénieur Bac + 5 - Bac S40
 
0.3%
Concours SESAME - Formation des écoles de commerce et de management36
 
0.3%
Concours Puissance Alpha - Formation d'ingénieur Bac + 5 - Bac +1/+231
 
0.2%
Concours Geipi Polytech - Formation d'ingénieur Bac + 5 - bac STI2D28
 
0.2%
Concours Puissance Alpha - Formation d'ingénieur Bac + 5 - bac STI2D22
 
0.2%
CESI Ecole d?Ingénieurs ? Direction générale (Paris La Défense) - Formation d'ingénieur Bac + 5 - Cycle Préparatoire Intégré - Spécialité Généraliste, BTP, Informatique, Systèmes embarqués22
 
0.2%
Regroupement d'IFSI Université Lille - D.E Infirmier22
 
0.2%
Concours Advance - Formation d'ingénieur Bac + 5 - Bac S17
 
0.1%
Regroupement d'IFSI Université Aix-Marseille - D.E Infirmier16
 
0.1%
Other values (11115)12477
97.8%
2021-03-18T16:43:35.782991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
38817
22.4%
de7980
 
4.6%
lycée6417
 
3.7%
et5698
 
3.3%
bts5410
 
3.1%
université3560
 
2.1%
licence3513
 
2.0%
services3120
 
1.8%
production1957
 
1.1%
la1839
 
1.1%
Other values (4880)94839
54.8%

Most occurring characters

ValueCountFrequency (%)
160670
14.7%
e111386
 
10.2%
i78947
 
7.2%
n60640
 
5.5%
t55739
 
5.1%
r51354
 
4.7%
s48559
 
4.4%
o48225
 
4.4%
a47722
 
4.4%
c42069
 
3.8%
Other values (86)390028
35.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter742607
67.8%
Space Separator160675
 
14.7%
Uppercase Letter135683
 
12.4%
Dash Punctuation39572
 
3.6%
Other Punctuation11016
 
1.0%
Decimal Number2178
 
0.2%
Close Punctuation1491
 
0.1%
Open Punctuation1490
 
0.1%
Math Symbol626
 
0.1%
Connector Punctuation1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
e111386
15.0%
i78947
10.6%
n60640
 
8.2%
t55739
 
7.5%
r51354
 
6.9%
s48559
 
6.5%
o48225
 
6.5%
a47722
 
6.4%
c42069
 
5.7%
l31393
 
4.2%
Other values (30)166573
22.4%
ValueCountFrequency (%)
S19217
14.2%
L15941
11.7%
T10454
 
7.7%
P10072
 
7.4%
C8921
 
6.6%
B8915
 
6.6%
E8903
 
6.6%
A8167
 
6.0%
M6424
 
4.7%
U6392
 
4.7%
Other values (19)32277
23.8%
ValueCountFrequency (%)
1582
26.7%
2484
22.2%
3479
22.0%
5430
19.7%
074
 
3.4%
860
 
2.8%
436
 
1.7%
619
 
0.9%
713
 
0.6%
91
 
< 0.1%
ValueCountFrequency (%)
'3481
31.6%
.2838
25.8%
,2371
21.5%
:743
 
6.7%
/726
 
6.6%
&587
 
5.3%
?234
 
2.1%
"36
 
0.3%
ValueCountFrequency (%)
160670
> 99.9%
 5
 
< 0.1%
ValueCountFrequency (%)
(1469
98.6%
[21
 
1.4%
ValueCountFrequency (%)
)1469
98.5%
]22
 
1.5%
ValueCountFrequency (%)
-39572
100.0%
ValueCountFrequency (%)
+626
100.0%
ValueCountFrequency (%)
_1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin878290
80.2%
Common217049
 
19.8%

Most frequent character per script

ValueCountFrequency (%)
e111386
 
12.7%
i78947
 
9.0%
n60640
 
6.9%
t55739
 
6.3%
r51354
 
5.8%
s48559
 
5.5%
o48225
 
5.5%
a47722
 
5.4%
c42069
 
4.8%
l31393
 
3.6%
Other values (59)302256
34.4%
ValueCountFrequency (%)
160670
74.0%
-39572
 
18.2%
'3481
 
1.6%
.2838
 
1.3%
,2371
 
1.1%
(1469
 
0.7%
)1469
 
0.7%
:743
 
0.3%
/726
 
0.3%
+626
 
0.3%
Other values (17)3084
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1064474
97.2%
None30865
 
2.8%

Most frequent character per block

ValueCountFrequency (%)
160670
15.1%
e111386
 
10.5%
i78947
 
7.4%
n60640
 
5.7%
t55739
 
5.2%
r51354
 
4.8%
s48559
 
4.6%
o48225
 
4.5%
a47722
 
4.5%
c42069
 
4.0%
Other values (68)359163
33.7%
ValueCountFrequency (%)
é26029
84.3%
è2659
 
8.6%
ô835
 
2.7%
à731
 
2.4%
ç195
 
0.6%
â174
 
0.6%
É104
 
0.3%
ê57
 
0.2%
î30
 
0.1%
ë19
 
0.1%
Other values (8)32
 
0.1%

form_lib_voe_acc
Categorical

Distinct50
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size99.8 KiB
BTS - Services
2883 
BTS - Production
1773 
Licence - Arts-lettres-langues
1155 
Licence - Sciences - technologies - santé
963 
Licence - Sciences humaines et sociales
594 
Other values (45)
5392 

Length

Max length73
Median length16
Mean length22.95509404
Min length3

Characters and Unicode

Total characters292907
Distinct characters49
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowD.E secteur sanitaire
2nd rowD.E secteur sanitaire
3rd rowD.E secteur sanitaire
4th rowD.E secteur sanitaire
5th rowD.E secteur sanitaire
ValueCountFrequency (%)
BTS - Services2883
22.6%
BTS - Production1773
13.9%
Licence - Arts-lettres-langues1155
 
9.1%
Licence - Sciences - technologies - santé963
 
7.5%
Licence - Sciences humaines et sociales594
 
4.7%
Licence - Droit-économie-gestion528
 
4.1%
BTS - Agricole511
 
4.0%
Mention complémentaire486
 
3.8%
DUT - Production479
 
3.8%
D.E secteur sanitaire468
 
3.7%
Other values (40)2920
22.9%
2021-03-18T16:43:36.187846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
11328
25.3%
bts5173
 
11.6%
licence3270
 
7.3%
services2883
 
6.4%
production2252
 
5.0%
sciences1610
 
3.6%
arts-lettres-langues1159
 
2.6%
et1014
 
2.3%
santé986
 
2.2%
technologies965
 
2.2%
Other values (69)14096
31.5%

Most occurring characters

ValueCountFrequency (%)
e35669
12.2%
32378
 
11.1%
i21907
 
7.5%
c21411
 
7.3%
s18418
 
6.3%
n16815
 
5.7%
r15407
 
5.3%
o14708
 
5.0%
-14673
 
5.0%
t14588
 
5.0%
Other values (39)86933
29.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter206668
70.6%
Uppercase Letter37712
 
12.9%
Space Separator32378
 
11.1%
Dash Punctuation14673
 
5.0%
Other Punctuation1402
 
0.5%
Math Symbol37
 
< 0.1%
Decimal Number37
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
e35669
17.3%
i21907
10.6%
c21411
10.4%
s18418
8.9%
n16815
8.1%
r15407
7.5%
o14708
7.1%
t14588
7.1%
a9316
 
4.5%
l7150
 
3.5%
Other values (14)31279
15.1%
ValueCountFrequency (%)
S10147
26.9%
T6044
16.0%
B5281
14.0%
L3264
 
8.7%
D2864
 
7.6%
P2442
 
6.5%
A2037
 
5.4%
C1176
 
3.1%
E1164
 
3.1%
M1033
 
2.7%
Other values (6)2260
 
6.0%
ValueCountFrequency (%)
.861
61.4%
'491
35.0%
,42
 
3.0%
/8
 
0.6%
ValueCountFrequency (%)
536
97.3%
31
 
2.7%
ValueCountFrequency (%)
32378
100.0%
ValueCountFrequency (%)
-14673
100.0%
ValueCountFrequency (%)
+37
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin244380
83.4%
Common48527
 
16.6%

Most frequent character per script

ValueCountFrequency (%)
e35669
14.6%
i21907
 
9.0%
c21411
 
8.8%
s18418
 
7.5%
n16815
 
6.9%
r15407
 
6.3%
o14708
 
6.0%
t14588
 
6.0%
S10147
 
4.2%
a9316
 
3.8%
Other values (30)65994
27.0%
ValueCountFrequency (%)
32378
66.7%
-14673
30.2%
.861
 
1.8%
'491
 
1.0%
,42
 
0.1%
+37
 
0.1%
536
 
0.1%
/8
 
< 0.1%
31
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII288225
98.4%
None4682
 
1.6%

Most frequent character per block

ValueCountFrequency (%)
e35669
12.4%
32378
 
11.2%
i21907
 
7.6%
c21411
 
7.4%
s18418
 
6.4%
n16815
 
5.8%
r15407
 
5.3%
o14708
 
5.1%
-14673
 
5.1%
t14588
 
5.1%
Other values (36)82251
28.5%
ValueCountFrequency (%)
é4550
97.2%
ô71
 
1.5%
à61
 
1.3%

regr_forma
Categorical

HIGH CARDINALITY
MISSING

Distinct53
Distinct (%)10.3%
Missing12247
Missing (%)96.0%
Memory size99.8 KiB
Concours Puissance Alpha
127 
Concours Geipi Polytech
71 
Concours SESAME
36 
Concours Avenir
27 
ECAM UniLaSalle Grandes Ecoles
26 
Other values (48)
226 

Length

Max length98
Median length24
Mean length25.77777778
Min length4

Characters and Unicode

Total characters13224
Distinct characters53
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)2.3%

Sample

1st rowCESI Ecole d?Ingénieurs ? Direction générale (Paris La Défense)
2nd rowCESI Ecole d?Ingénieurs ? Direction générale (Paris La Défense)
3rd rowCESI Ecole d?Ingénieurs ? Direction générale (Paris La Défense)
4th rowCESI Ecole d?Ingénieurs ? Direction générale (Paris La Défense)
5th rowRéseau ScPo ? concours commun
ValueCountFrequency (%)
Concours Puissance Alpha127
 
1.0%
Concours Geipi Polytech71
 
0.6%
Concours SESAME36
 
0.3%
Concours Avenir27
 
0.2%
ECAM UniLaSalle Grandes Ecoles26
 
0.2%
CESI Ecole d?Ingénieurs ? Direction générale (Paris La Défense)22
 
0.2%
Concours Advance20
 
0.2%
Groupe INSA16
 
0.1%
GROUPE ICAM13
 
0.1%
Concours ACCES9
 
0.1%
Other values (43)146
 
1.1%
(Missing)12247
96.0%
2021-03-18T16:43:36.422369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
concours332
 
18.0%
puissance127
 
6.9%
alpha127
 
6.9%
polytech71
 
3.8%
geipi71
 
3.8%
de68
 
3.7%
62
 
3.4%
groupe36
 
2.0%
sesame36
 
2.0%
la30
 
1.6%
Other values (112)886
48.0%

Most occurring characters

ValueCountFrequency (%)
1333
 
10.1%
e1065
 
8.1%
o1045
 
7.9%
s946
 
7.2%
n845
 
6.4%
c782
 
5.9%
r715
 
5.4%
u636
 
4.8%
a603
 
4.6%
i555
 
4.2%
Other values (43)4699
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9127
69.0%
Uppercase Letter2528
 
19.1%
Space Separator1333
 
10.1%
Other Punctuation105
 
0.8%
Dash Punctuation43
 
0.3%
Open Punctuation39
 
0.3%
Close Punctuation39
 
0.3%
Decimal Number10
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
e1065
11.7%
o1045
11.4%
s946
10.4%
n845
9.3%
c782
8.6%
r715
7.8%
u636
7.0%
a603
6.6%
i555
 
6.1%
l435
 
4.8%
Other values (14)1500
16.4%
ValueCountFrequency (%)
C492
19.5%
A351
13.9%
P334
13.2%
E276
10.9%
S266
10.5%
G145
 
5.7%
I125
 
4.9%
M106
 
4.2%
R92
 
3.6%
L72
 
2.8%
Other values (9)269
10.6%
ValueCountFrequency (%)
?51
48.6%
'36
34.3%
,15
 
14.3%
/2
 
1.9%
:1
 
1.0%
ValueCountFrequency (%)
1333
100.0%
ValueCountFrequency (%)
(39
100.0%
ValueCountFrequency (%)
)39
100.0%
ValueCountFrequency (%)
-43
100.0%
ValueCountFrequency (%)
310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin11655
88.1%
Common1569
 
11.9%

Most frequent character per script

ValueCountFrequency (%)
e1065
 
9.1%
o1045
 
9.0%
s946
 
8.1%
n845
 
7.3%
c782
 
6.7%
r715
 
6.1%
u636
 
5.5%
a603
 
5.2%
i555
 
4.8%
C492
 
4.2%
Other values (33)3971
34.1%
ValueCountFrequency (%)
1333
85.0%
?51
 
3.3%
-43
 
2.7%
(39
 
2.5%
)39
 
2.5%
'36
 
2.3%
,15
 
1.0%
310
 
0.6%
/2
 
0.1%
:1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII13078
98.9%
None146
 
1.1%

Most frequent character per block

ValueCountFrequency (%)
1333
 
10.2%
e1065
 
8.1%
o1045
 
8.0%
s946
 
7.2%
n845
 
6.5%
c782
 
6.0%
r715
 
5.5%
u636
 
4.9%
a603
 
4.6%
i555
 
4.2%
Other values (41)4553
34.8%
ValueCountFrequency (%)
é143
97.9%
É3
 
2.1%

fil_lib_voe_acc
Categorical

HIGH CARDINALITY

Distinct517
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size99.8 KiB
Langues, littératures & civilisations étrangères et régionales
 
480
Formation d'ingénieur Bac + 5
 
384
Comptabilité et gestion
 
356
Management Commercial Opérationnel
 
349
D.E Infirmier
 
329
Other values (512)
10862 

Length

Max length119
Median length29
Mean length30.70242947
Min length2

Characters and Unicode

Total characters391763
Distinct characters78
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique192 ?
Unique (%)1.5%

Sample

1st rowD.E Infirmier
2nd rowD.E Infirmier
3rd rowD.E Infirmier
4th rowD.E Infirmier
5th rowD.E Infirmier
ValueCountFrequency (%)
Langues, littératures & civilisations étrangères et régionales480
 
3.8%
Formation d'ingénieur Bac + 5384
 
3.0%
Comptabilité et gestion356
 
2.8%
Management Commercial Opérationnel349
 
2.7%
D.E Infirmier329
 
2.6%
Langues étrangères appliquées322
 
2.5%
Gestion de la PME276
 
2.2%
Droit258
 
2.0%
Support à l'action managériale255
 
2.0%
Lettres253
 
2.0%
Other values (507)9498
74.4%
2021-03-18T16:43:36.648596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
et4150
 
8.1%
de2919
 
5.7%
2254
 
4.4%
gestion1181
 
2.3%
des1164
 
2.3%
option1141
 
2.2%
la1066
 
2.1%
langues810
 
1.6%
étrangères802
 
1.6%
sciences707
 
1.4%
Other values (777)35068
68.4%

Most occurring characters

ValueCountFrequency (%)
e41785
 
10.7%
38503
 
9.8%
i35314
 
9.0%
t29426
 
7.5%
n25453
 
6.5%
s22482
 
5.7%
o22395
 
5.7%
a22305
 
5.7%
r19300
 
4.9%
c14325
 
3.7%
Other values (68)120475
30.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter319204
81.5%
Space Separator38503
 
9.8%
Uppercase Letter25720
 
6.6%
Other Punctuation4103
 
1.0%
Dash Punctuation1598
 
0.4%
Decimal Number872
 
0.2%
Math Symbol593
 
0.2%
Open Punctuation585
 
0.1%
Close Punctuation585
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
e41785
13.1%
i35314
11.1%
t29426
9.2%
n25453
 
8.0%
s22482
 
7.0%
o22395
 
7.0%
a22305
 
7.0%
r19300
 
6.0%
c14325
 
4.5%
l13082
 
4.1%
Other values (24)73337
23.0%
ValueCountFrequency (%)
S4210
16.4%
C2811
10.9%
E2290
 
8.9%
A1875
 
7.3%
P1790
 
7.0%
M1695
 
6.6%
L1421
 
5.5%
I1255
 
4.9%
O1246
 
4.8%
T1241
 
4.8%
Other values (14)5886
22.9%
ValueCountFrequency (%)
'1693
41.3%
,1035
25.2%
.656
 
16.0%
&481
 
11.7%
:143
 
3.5%
/87
 
2.1%
?6
 
0.1%
"2
 
< 0.1%
ValueCountFrequency (%)
5438
50.2%
1125
 
14.3%
3120
 
13.8%
298
 
11.2%
066
 
7.6%
424
 
2.8%
61
 
0.1%
ValueCountFrequency (%)
38503
100.0%
ValueCountFrequency (%)
-1598
100.0%
ValueCountFrequency (%)
+593
100.0%
ValueCountFrequency (%)
(585
100.0%
ValueCountFrequency (%)
)585
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin344924
88.0%
Common46839
 
12.0%

Most frequent character per script

ValueCountFrequency (%)
e41785
12.1%
i35314
 
10.2%
t29426
 
8.5%
n25453
 
7.4%
s22482
 
6.5%
o22395
 
6.5%
a22305
 
6.5%
r19300
 
5.6%
c14325
 
4.2%
l13082
 
3.8%
Other values (48)99057
28.7%
ValueCountFrequency (%)
38503
82.2%
'1693
 
3.6%
-1598
 
3.4%
,1035
 
2.2%
.656
 
1.4%
+593
 
1.3%
(585
 
1.2%
)585
 
1.2%
&481
 
1.0%
5438
 
0.9%
Other values (10)672
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII378463
96.6%
None13300
 
3.4%

Most frequent character per block

ValueCountFrequency (%)
e41785
 
11.0%
38503
 
10.2%
i35314
 
9.3%
t29426
 
7.8%
n25453
 
6.7%
s22482
 
5.9%
o22395
 
5.9%
a22305
 
5.9%
r19300
 
5.1%
c14325
 
3.8%
Other values (58)107175
28.3%
ValueCountFrequency (%)
é10101
75.9%
è2076
 
15.6%
à535
 
4.0%
ô393
 
3.0%
â122
 
0.9%
ê37
 
0.3%
É27
 
0.2%
ç6
 
< 0.1%
ë2
 
< 0.1%
û1
 
< 0.1%

detail_forma
Categorical

HIGH CARDINALITY
MISSING

Distinct2410
Distinct (%)70.9%
Missing9361
Missing (%)73.4%
Memory size99.8 KiB
Bac S
 
168
bac STI2D
 
63
Anglais
 
37
Bac +1/+2
 
30
Espagnol
 
29
Other values (2405)
3072 

Length

Max length150
Median length35
Mean length40.93968814
Min length3

Characters and Unicode

Total characters139154
Distinct characters94
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2043 ?
Unique (%)60.1%

Sample

1st rowChimie-Physique entièrement enseigné en anglais
2nd rowInternational Droit français-droit anglais
3rd rowGéographie / Histoire : Géographie et aménagement
4th rowGéographie / Sociologie : Géographie et aménagement
5th rowLicence 1 Histoire - Enseignement dispensé à distance
ValueCountFrequency (%)
Bac S168
 
1.3%
bac STI2D63
 
0.5%
Anglais37
 
0.3%
Bac +1/+230
 
0.2%
Espagnol29
 
0.2%
Cycle Préparatoire Intégré - Spécialité Généraliste, BTP, Informatique, Systèmes embarqués22
 
0.2%
Lettres modernes16
 
0.1%
bac STL16
 
0.1%
Cycle préparatoire intégré15
 
0.1%
Bac ES - Option Maths13
 
0.1%
Other values (2400)2990
 
23.4%
(Missing)9361
73.4%
2021-03-18T16:43:36.886699image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2334
 
12.1%
et1132
 
5.8%
de625
 
3.2%
sciences427
 
2.2%
parcours368
 
1.9%
bac348
 
1.8%
spécialité347
 
1.8%
anglais304
 
1.6%
langues285
 
1.5%
la265
 
1.4%
Other values (1784)12916
66.7%

Most occurring characters

ValueCountFrequency (%)
16084
 
11.6%
e13414
 
9.6%
i10910
 
7.8%
n8414
 
6.0%
a8214
 
5.9%
t7715
 
5.5%
s7456
 
5.4%
r6557
 
4.7%
o5751
 
4.1%
c4692
 
3.4%
Other values (84)49947
35.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter101431
72.9%
Space Separator16090
 
11.6%
Uppercase Letter15753
 
11.3%
Other Punctuation2687
 
1.9%
Dash Punctuation1831
 
1.3%
Close Punctuation442
 
0.3%
Open Punctuation441
 
0.3%
Decimal Number399
 
0.3%
Math Symbol80
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
e13414
13.2%
i10910
10.8%
n8414
 
8.3%
a8214
 
8.1%
t7715
 
7.6%
s7456
 
7.4%
r6557
 
6.5%
o5751
 
5.7%
c4692
 
4.6%
l4669
 
4.6%
Other values (29)23639
23.3%
ValueCountFrequency (%)
S1990
12.6%
E1692
10.7%
A1568
10.0%
L1448
9.2%
I1182
 
7.5%
C1137
 
7.2%
M1012
 
6.4%
P991
 
6.3%
D727
 
4.6%
T698
 
4.4%
Other values (18)3308
21.0%
ValueCountFrequency (%)
1161
40.4%
2155
38.8%
337
 
9.3%
417
 
4.3%
511
 
2.8%
86
 
1.5%
06
 
1.5%
63
 
0.8%
72
 
0.5%
91
 
0.3%
ValueCountFrequency (%)
,954
35.5%
:708
26.3%
/408
15.2%
'343
 
12.8%
&90
 
3.3%
?74
 
2.8%
.56
 
2.1%
"52
 
1.9%
%2
 
0.1%
ValueCountFrequency (%)
16084
> 99.9%
 6
 
< 0.1%
ValueCountFrequency (%)
(420
95.2%
[21
 
4.8%
ValueCountFrequency (%)
)420
95.0%
]22
 
5.0%
ValueCountFrequency (%)
-1831
100.0%
ValueCountFrequency (%)
+80
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin117184
84.2%
Common21970
 
15.8%

Most frequent character per script

ValueCountFrequency (%)
e13414
 
11.4%
i10910
 
9.3%
n8414
 
7.2%
a8214
 
7.0%
t7715
 
6.6%
s7456
 
6.4%
r6557
 
5.6%
o5751
 
4.9%
c4692
 
4.0%
l4669
 
4.0%
Other values (57)39392
33.6%
ValueCountFrequency (%)
16084
73.2%
-1831
 
8.3%
,954
 
4.3%
:708
 
3.2%
(420
 
1.9%
)420
 
1.9%
/408
 
1.9%
'343
 
1.6%
1161
 
0.7%
2155
 
0.7%
Other values (17)486
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII135176
97.1%
None3978
 
2.9%

Most frequent character per block

ValueCountFrequency (%)
16084
 
11.9%
e13414
 
9.9%
i10910
 
8.1%
n8414
 
6.2%
a8214
 
6.1%
t7715
 
5.7%
s7456
 
5.5%
r6557
 
4.9%
o5751
 
4.3%
c4692
 
3.5%
Other values (68)45969
34.0%
ValueCountFrequency (%)
é3298
82.9%
è297
 
7.5%
à124
 
3.1%
ô104
 
2.6%
É52
 
1.3%
ç43
 
1.1%
â23
 
0.6%
È7
 
0.2%
 6
 
0.2%
ï5
 
0.1%
Other values (6)19
 
0.5%

lien_form_psup
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct12580
Distinct (%)100.0%
Missing180
Missing (%)1.4%
Memory size99.8 KiB
https://dossier.parcoursup.fr/Candidat/carte?ACTION=2&g_ti_cod=27845&g_ta_cod=27884
 
1
https://dossier.parcoursup.fr/Candidat/carte?ACTION=2&g_ti_cod=6278&g_ta_cod=6278
 
1
https://dossier.parcoursup.fr/Candidat/carte?ACTION=2&g_ti_cod=13609&g_ta_cod=13609
 
1
https://dossier.parcoursup.fr/Candidat/carte?ACTION=2&g_ti_cod=11024&g_ta_cod=11024
 
1
https://dossier.parcoursup.fr/Candidat/carte?ACTION=2&g_ti_cod=24158&g_ta_cod=24158
 
1
Other values (12575)
12575 

Length

Max length83
Median length83
Mean length82.01661367
Min length75

Characters and Unicode

Total characters1031769
Distinct characters38
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12580 ?
Unique (%)100.0%

Sample

1st rowhttps://dossier.parcoursup.fr/Candidat/carte?ACTION=2&g_ti_cod=25148&g_ta_cod=23207
2nd rowhttps://dossier.parcoursup.fr/Candidat/carte?ACTION=2&g_ti_cod=25148&g_ta_cod=23209
3rd rowhttps://dossier.parcoursup.fr/Candidat/carte?ACTION=2&g_ti_cod=22988&g_ta_cod=23210
4th rowhttps://dossier.parcoursup.fr/Candidat/carte?ACTION=2&g_ti_cod=22993&g_ta_cod=23233
5th rowhttps://dossier.parcoursup.fr/Candidat/carte?ACTION=2&g_ti_cod=22985&g_ta_cod=23247
ValueCountFrequency (%)
https://dossier.parcoursup.fr/Candidat/carte?ACTION=2&g_ti_cod=27845&g_ta_cod=278841
 
< 0.1%
https://dossier.parcoursup.fr/Candidat/carte?ACTION=2&g_ti_cod=6278&g_ta_cod=62781
 
< 0.1%
https://dossier.parcoursup.fr/Candidat/carte?ACTION=2&g_ti_cod=13609&g_ta_cod=136091
 
< 0.1%
https://dossier.parcoursup.fr/Candidat/carte?ACTION=2&g_ti_cod=11024&g_ta_cod=110241
 
< 0.1%
https://dossier.parcoursup.fr/Candidat/carte?ACTION=2&g_ti_cod=24158&g_ta_cod=241581
 
< 0.1%
https://dossier.parcoursup.fr/Candidat/carte?ACTION=2&g_ti_cod=19202&g_ta_cod=192021
 
< 0.1%
https://dossier.parcoursup.fr/Candidat/carte?ACTION=2&g_ti_cod=6835&g_ta_cod=68351
 
< 0.1%
https://dossier.parcoursup.fr/Candidat/carte?ACTION=2&g_ti_cod=9699&g_ta_cod=96991
 
< 0.1%
https://dossier.parcoursup.fr/Candidat/carte?ACTION=2&g_ti_cod=18723&g_ta_cod=187231
 
< 0.1%
https://dossier.parcoursup.fr/Candidat/carte?ACTION=2&g_ti_cod=27513&g_ta_cod=41841
 
< 0.1%
Other values (12570)12570
98.5%
(Missing)180
 
1.4%
2021-03-18T16:43:37.123728image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://dossier.parcoursup.fr/candidat/carte?action=2&g_ti_cod=20593&g_ta_cod=205931
 
< 0.1%
https://dossier.parcoursup.fr/candidat/carte?action=2&g_ti_cod=19770&g_ta_cod=197701
 
< 0.1%
https://dossier.parcoursup.fr/candidat/carte?action=2&g_ti_cod=4160&g_ta_cod=41601
 
< 0.1%
https://dossier.parcoursup.fr/candidat/carte?action=2&g_ti_cod=27725&g_ta_cod=277261
 
< 0.1%
https://dossier.parcoursup.fr/candidat/carte?action=2&g_ti_cod=6345&g_ta_cod=63451
 
< 0.1%
https://dossier.parcoursup.fr/candidat/carte?action=2&g_ti_cod=28068&g_ta_cod=280731
 
< 0.1%
https://dossier.parcoursup.fr/candidat/carte?action=2&g_ti_cod=24120&g_ta_cod=241201
 
< 0.1%
https://dossier.parcoursup.fr/candidat/carte?action=2&g_ti_cod=7375&g_ta_cod=73751
 
< 0.1%
https://dossier.parcoursup.fr/candidat/carte?action=2&g_ti_cod=7978&g_ta_cod=79781
 
< 0.1%
https://dossier.parcoursup.fr/candidat/carte?action=2&g_ti_cod=3183&g_ta_cod=31831
 
< 0.1%
Other values (12570)12570
99.9%

Most occurring characters

ValueCountFrequency (%)
t75480
 
7.3%
d62900
 
6.1%
r62900
 
6.1%
a62900
 
6.1%
s50320
 
4.9%
/50320
 
4.9%
o50320
 
4.9%
c50320
 
4.9%
_50320
 
4.9%
p37740
 
3.7%
Other values (28)478249
46.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter603840
58.5%
Decimal Number126009
 
12.2%
Other Punctuation125800
 
12.2%
Uppercase Letter88060
 
8.5%
Connector Punctuation50320
 
4.9%
Math Symbol37740
 
3.7%

Most frequent character per category

ValueCountFrequency (%)
t75480
12.5%
d62900
10.4%
r62900
10.4%
a62900
10.4%
s50320
8.3%
o50320
8.3%
c50320
8.3%
p37740
6.2%
i37740
6.2%
e25160
 
4.2%
Other values (5)88060
14.6%
ValueCountFrequency (%)
229842
23.7%
115499
12.3%
310308
 
8.2%
910256
 
8.1%
410243
 
8.1%
610234
 
8.1%
810036
 
8.0%
79968
 
7.9%
09960
 
7.9%
59663
 
7.7%
ValueCountFrequency (%)
C25160
28.6%
A12580
14.3%
T12580
14.3%
I12580
14.3%
O12580
14.3%
N12580
14.3%
ValueCountFrequency (%)
/50320
40.0%
.25160
20.0%
&25160
20.0%
:12580
 
10.0%
?12580
 
10.0%
ValueCountFrequency (%)
=37740
100.0%
ValueCountFrequency (%)
_50320
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin691900
67.1%
Common339869
32.9%

Most frequent character per script

ValueCountFrequency (%)
t75480
10.9%
d62900
 
9.1%
r62900
 
9.1%
a62900
 
9.1%
s50320
 
7.3%
o50320
 
7.3%
c50320
 
7.3%
p37740
 
5.5%
i37740
 
5.5%
e25160
 
3.6%
Other values (11)176120
25.5%
ValueCountFrequency (%)
/50320
14.8%
_50320
14.8%
=37740
11.1%
229842
8.8%
.25160
 
7.4%
&25160
 
7.4%
115499
 
4.6%
:12580
 
3.7%
?12580
 
3.7%
310308
 
3.0%
Other values (7)70360
20.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1031769
100.0%

Most frequent character per block

ValueCountFrequency (%)
t75480
 
7.3%
d62900
 
6.1%
r62900
 
6.1%
a62900
 
6.1%
s50320
 
4.9%
/50320
 
4.9%
o50320
 
4.9%
c50320
 
4.9%
_50320
 
4.9%
p37740
 
3.7%
Other values (28)478249
46.4%

g_olocalisation_des_formations
Categorical

HIGH CARDINALITY
MISSING

Distinct4930
Distinct (%)39.2%
Missing179
Missing (%)1.4%
Memory size99.8 KiB
48.8452,2.39666
 
84
48.9035,2.21449
 
83
48.8274,2.37623
 
76
43.5154,5.44724
 
58
49.1889,-0.36388
 
57
Other values (4925)
12223 

Length

Max length20
Median length15
Mean length15.07670296
Min length11

Characters and Unicode

Total characters189680
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2792 ?
Unique (%)22.2%

Sample

1st row48.8297,2.33989
2nd row48.81977,2.42976
3rd row45.6181,0.1137
4th row45.4691,4.50954
5th row43.1127,1.60858
ValueCountFrequency (%)
48.8452,2.3966684
 
0.7%
48.9035,2.2144983
 
0.7%
48.8274,2.3762376
 
0.6%
43.5154,5.4472458
 
0.5%
49.1889,-0.3638857
 
0.4%
48.7885,2.4441856
 
0.4%
48.6959,6.1766751
 
0.4%
43.6326,3.8692550
 
0.4%
48.9458,2.3634149
 
0.4%
48.12162,-1.7044548
 
0.4%
Other values (4920)11969
93.8%
(Missing)179
 
1.4%
2021-03-18T16:43:37.334922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
48.8452,2.3966684
 
0.7%
48.9035,2.2144983
 
0.7%
48.8274,2.3762376
 
0.6%
43.5154,5.4472458
 
0.5%
49.1889,-0.3638857
 
0.5%
48.7885,2.4441856
 
0.4%
48.6959,6.1766751
 
0.4%
43.6326,3.8692550
 
0.4%
48.9458,2.3634149
 
0.4%
47.2405,6.0227148
 
0.4%
Other values (4920)11969
95.1%

Most occurring characters

ValueCountFrequency (%)
425420
13.4%
.25162
13.3%
815719
8.3%
514984
7.9%
214919
7.9%
314674
7.7%
113763
7.3%
613715
7.2%
713605
7.2%
,12581
6.6%
Other values (3)25138
13.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number149125
78.6%
Other Punctuation37743
 
19.9%
Dash Punctuation2812
 
1.5%

Most frequent character per category

ValueCountFrequency (%)
425420
17.0%
815719
10.5%
514984
10.0%
214919
10.0%
314674
9.8%
113763
9.2%
613715
9.2%
713605
9.1%
911742
7.9%
010584
7.1%
ValueCountFrequency (%)
.25162
66.7%
,12581
33.3%
ValueCountFrequency (%)
-2812
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common189680
100.0%

Most frequent character per script

ValueCountFrequency (%)
425420
13.4%
.25162
13.3%
815719
8.3%
514984
7.9%
214919
7.9%
314674
7.7%
113763
7.3%
613715
7.2%
713605
7.2%
,12581
6.6%
Other values (3)25138
13.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII189680
100.0%

Most frequent character per block

ValueCountFrequency (%)
425420
13.4%
.25162
13.3%
815719
8.3%
514984
7.9%
214919
7.9%
314674
7.7%
113763
7.3%
613715
7.2%
713605
7.2%
,12581
6.6%
Other values (3)25138
13.3%

capa_fin
Real number (ℝ≥0)

Distinct359
Distinct (%)2.8%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean58.94615565
Minimum1
Maximum3400
Zeros0
Zeros (%)0.0%
Memory size99.8 KiB
2021-03-18T16:43:37.429499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q120
median33
Q355
95-th percentile185
Maximum3400
Range3399
Interquartile range (IQR)35

Descriptive statistics

Standard deviation108.0732328
Coefficient of variation (CV)1.833422919
Kurtosis224.1823724
Mean58.94615565
Median Absolute Deviation (MAD)15
Skewness11.22016114
Sum752094
Variance11679.82364
MonotocityNot monotonic
2021-03-18T16:43:37.528209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
351064
 
8.3%
241020
 
8.0%
151010
 
7.9%
30896
 
7.0%
48399
 
3.1%
20393
 
3.1%
40379
 
3.0%
12379
 
3.0%
18306
 
2.4%
32281
 
2.2%
Other values (349)6632
52.0%
ValueCountFrequency (%)
15
 
< 0.1%
232
 
0.3%
335
 
0.3%
437
 
0.3%
597
0.8%
ValueCountFrequency (%)
34001
 
< 0.1%
32001
 
< 0.1%
24003
< 0.1%
20002
< 0.1%
16002
< 0.1%

voe_tot
Real number (ℝ≥0)

ZEROS

Distinct2820
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean829.4266458
Minimum0
Maximum16266
Zeros195
Zeros (%)1.5%
Memory size99.8 KiB
2021-03-18T16:43:37.631964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile54
Q1194
median414
Q3903
95-th percentile3109.15
Maximum16266
Range16266
Interquartile range (IQR)709

Descriptive statistics

Standard deviation1232.570749
Coefficient of variation (CV)1.486051546
Kurtosis22.39841694
Mean829.4266458
Median Absolute Deviation (MAD)273
Skewness3.94502314
Sum10583484
Variance1519230.652
MonotocityNot monotonic
2021-03-18T16:43:37.731374image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0195
 
1.5%
12736
 
0.3%
12632
 
0.3%
29131
 
0.2%
14631
 
0.2%
26730
 
0.2%
11026
 
0.2%
17526
 
0.2%
27025
 
0.2%
25425
 
0.2%
Other values (2810)12303
96.4%
ValueCountFrequency (%)
0195
1.5%
18
 
0.1%
24
 
< 0.1%
32
 
< 0.1%
42
 
< 0.1%
ValueCountFrequency (%)
162661
< 0.1%
150081
< 0.1%
149681
< 0.1%
132131
< 0.1%
131521
< 0.1%

voe_tot_f
Real number (ℝ≥0)

ZEROS

Distinct2024
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean469.8552508
Minimum0
Maximum10124
Zeros262
Zeros (%)2.1%
Memory size99.8 KiB
2021-03-18T16:43:37.840172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q160
median201
Q3487
95-th percentile1951
Maximum10124
Range10124
Interquartile range (IQR)427

Descriptive statistics

Standard deviation849.0437857
Coefficient of variation (CV)1.807032664
Kurtosis26.05613622
Mean469.8552508
Median Absolute Deviation (MAD)167
Skewness4.424701905
Sum5995353
Variance720875.3501
MonotocityNot monotonic
2021-03-18T16:43:37.945574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0262
 
2.1%
598
 
0.8%
393
 
0.7%
791
 
0.7%
690
 
0.7%
885
 
0.7%
284
 
0.7%
482
 
0.6%
980
 
0.6%
1076
 
0.6%
Other values (2014)11719
91.8%
ValueCountFrequency (%)
0262
2.1%
167
 
0.5%
284
 
0.7%
393
 
0.7%
482
 
0.6%
ValueCountFrequency (%)
101241
< 0.1%
95701
< 0.1%
93741
< 0.1%
88801
< 0.1%
88421
< 0.1%

nb_voe_pp
Real number (ℝ≥0)

ZEROS

Distinct2779
Distinct (%)21.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean794.9484326
Minimum0
Maximum16266
Zeros227
Zeros (%)1.8%
Memory size99.8 KiB
2021-03-18T16:43:38.047313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile38
Q1167
median375
Q3853
95-th percentile3098.2
Maximum16266
Range16266
Interquartile range (IQR)686

Descriptive statistics

Standard deviation1233.402317
Coefficient of variation (CV)1.551550097
Kurtosis22.68180211
Mean794.9484326
Median Absolute Deviation (MAD)258
Skewness3.986055414
Sum10143542
Variance1521281.276
MonotocityNot monotonic
2021-03-18T16:43:38.148243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0227
 
1.8%
9530
 
0.2%
14130
 
0.2%
13928
 
0.2%
6428
 
0.2%
12628
 
0.2%
8328
 
0.2%
14028
 
0.2%
13727
 
0.2%
19627
 
0.2%
Other values (2769)12279
96.2%
ValueCountFrequency (%)
0227
1.8%
13
 
< 0.1%
28
 
0.1%
38
 
0.1%
43
 
< 0.1%
ValueCountFrequency (%)
162661
< 0.1%
150081
< 0.1%
149681
< 0.1%
132131
< 0.1%
131521
< 0.1%

nb_voe_pp_internat
Real number (ℝ≥0)

MISSING
ZEROS

Distinct483
Distinct (%)57.0%
Missing11913
Missing (%)93.4%
Infinite0
Infinite (%)0.0%
Mean471.9634002
Minimum0
Maximum4599
Zeros196
Zeros (%)1.5%
Memory size99.8 KiB
2021-03-18T16:43:38.253487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q159
median248
Q3629.5
95-th percentile1759.3
Maximum4599
Range4599
Interquartile range (IQR)570.5

Descriptive statistics

Standard deviation646.0272464
Coefficient of variation (CV)1.368807933
Kurtosis10.20158805
Mean471.9634002
Median Absolute Deviation (MAD)248
Skewness2.769727464
Sum399753
Variance417351.2032
MonotocityNot monotonic
2021-03-18T16:43:38.351493image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0196
 
1.5%
2455
 
< 0.1%
1904
 
< 0.1%
1974
 
< 0.1%
1054
 
< 0.1%
5994
 
< 0.1%
2154
 
< 0.1%
1684
 
< 0.1%
2233
 
< 0.1%
353
 
< 0.1%
Other values (473)616
 
4.8%
(Missing)11913
93.4%
ValueCountFrequency (%)
0196
1.5%
221
 
< 0.1%
241
 
< 0.1%
291
 
< 0.1%
353
 
< 0.1%
ValueCountFrequency (%)
45991
< 0.1%
44261
< 0.1%
43691
< 0.1%
39521
< 0.1%
39461
< 0.1%

nb_voe_pp_bg
Real number (ℝ≥0)

ZEROS

Distinct1947
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean380.8802508
Minimum0
Maximum14914
Zeros715
Zeros (%)5.6%
Memory size99.8 KiB
2021-03-18T16:43:38.670121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q125
median97
Q3382
95-th percentile1707
Maximum14914
Range14914
Interquartile range (IQR)357

Descriptive statistics

Standard deviation810.8479011
Coefficient of variation (CV)2.128878826
Kurtosis52.1580888
Mean380.8802508
Median Absolute Deviation (MAD)88
Skewness5.746534744
Sum4860032
Variance657474.3188
MonotocityNot monotonic
2021-03-18T16:43:38.774367image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0715
 
5.6%
7122
 
1.0%
6121
 
0.9%
4116
 
0.9%
11109
 
0.9%
19108
 
0.8%
8107
 
0.8%
9105
 
0.8%
22105
 
0.8%
1103
 
0.8%
Other values (1937)11049
86.6%
ValueCountFrequency (%)
0715
5.6%
1103
 
0.8%
2101
 
0.8%
394
 
0.7%
4116
 
0.9%
ValueCountFrequency (%)
149141
< 0.1%
137401
< 0.1%
121771
< 0.1%
114571
< 0.1%
109061
< 0.1%

nb_voe_pp_bg_brs
Real number (ℝ≥0)

ZEROS

Distinct606
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.04741379
Minimum0
Maximum2382
Zeros1198
Zeros (%)9.4%
Memory size99.8 KiB
2021-03-18T16:43:38.875769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median19
Q365
95-th percentile265
Maximum2382
Range2382
Interquartile range (IQR)60

Descriptive statistics

Standard deviation124.3653926
Coefficient of variation (CV)2.004360616
Kurtosis44.99473715
Mean62.04741379
Median Absolute Deviation (MAD)18
Skewness5.35597089
Sum791725
Variance15466.75087
MonotocityNot monotonic
2021-03-18T16:43:38.977291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01198
 
9.4%
1579
 
4.5%
2486
 
3.8%
3446
 
3.5%
4423
 
3.3%
5343
 
2.7%
6327
 
2.6%
8289
 
2.3%
7289
 
2.3%
9255
 
2.0%
Other values (596)8125
63.7%
ValueCountFrequency (%)
01198
9.4%
1579
4.5%
2486
3.8%
3446
 
3.5%
4423
 
3.3%
ValueCountFrequency (%)
23821
< 0.1%
19891
< 0.1%
16471
< 0.1%
15311
< 0.1%
15181
< 0.1%

nb_voe_pp_bt
Real number (ℝ≥0)

ZEROS

Distinct905
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.4277429
Minimum0
Maximum2553
Zeros960
Zeros (%)7.5%
Memory size99.8 KiB
2021-03-18T16:43:39.074286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q115
median61
Q3150
95-th percentile505
Maximum2553
Range2553
Interquartile range (IQR)135

Descriptive statistics

Standard deviation198.5220699
Coefficient of variation (CV)1.55791875
Kurtosis20.12959743
Mean127.4277429
Median Absolute Deviation (MAD)55
Skewness3.706292702
Sum1625978
Variance39411.01226
MonotocityNot monotonic
2021-03-18T16:43:39.177745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0960
 
7.5%
1403
 
3.2%
2282
 
2.2%
3224
 
1.8%
4162
 
1.3%
5154
 
1.2%
7121
 
0.9%
6115
 
0.9%
8113
 
0.9%
10110
 
0.9%
Other values (895)10116
79.3%
ValueCountFrequency (%)
0960
7.5%
1403
3.2%
2282
 
2.2%
3224
 
1.8%
4162
 
1.3%
ValueCountFrequency (%)
25531
< 0.1%
24461
< 0.1%
23951
< 0.1%
20971
< 0.1%
18831
< 0.1%

nb_voe_pp_bt_brs
Real number (ℝ≥0)

ZEROS

Distinct410
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.85626959
Minimum0
Maximum1159
Zeros1618
Zeros (%)12.7%
Memory size99.8 KiB
2021-03-18T16:43:39.277075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.75
median15
Q343
95-th percentile164
Maximum1159
Range1159
Interquartile range (IQR)39.25

Descriptive statistics

Standard deviation67.68716878
Coefficient of variation (CV)1.741988346
Kurtosis27.02365922
Mean38.85626959
Median Absolute Deviation (MAD)14
Skewness4.188971778
Sum495806
Variance4581.552817
MonotocityNot monotonic
2021-03-18T16:43:39.381046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01618
 
12.7%
1668
 
5.2%
2479
 
3.8%
3425
 
3.3%
6352
 
2.8%
5337
 
2.6%
4336
 
2.6%
7303
 
2.4%
8278
 
2.2%
10277
 
2.2%
Other values (400)7687
60.2%
ValueCountFrequency (%)
01618
12.7%
1668
5.2%
2479
 
3.8%
3425
 
3.3%
4336
 
2.6%
ValueCountFrequency (%)
11591
< 0.1%
8501
< 0.1%
7481
< 0.1%
7211
< 0.1%
7131
< 0.1%

nb_voe_pp_bp
Real number (ℝ≥0)

ZEROS

Distinct590
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.84851097
Minimum0
Maximum1446
Zeros1799
Zeros (%)14.1%
Memory size99.8 KiB
2021-03-18T16:43:39.485617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median26
Q375
95-th percentile265
Maximum1446
Range1446
Interquartile range (IQR)72

Descriptive statistics

Standard deviation107.4273958
Coefficient of variation (CV)1.682535648
Kurtosis18.83954956
Mean63.84851097
Median Absolute Deviation (MAD)25
Skewness3.673536485
Sum814707
Variance11540.64536
MonotocityNot monotonic
2021-03-18T16:43:39.584437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01799
 
14.1%
1655
 
5.1%
2467
 
3.7%
3332
 
2.6%
4245
 
1.9%
6227
 
1.8%
5224
 
1.8%
7207
 
1.6%
8182
 
1.4%
11147
 
1.2%
Other values (580)8275
64.9%
ValueCountFrequency (%)
01799
14.1%
1655
 
5.1%
2467
 
3.7%
3332
 
2.6%
4245
 
1.9%
ValueCountFrequency (%)
14461
< 0.1%
11061
< 0.1%
10871
< 0.1%
10311
< 0.1%
10131
< 0.1%

nb_voe_pp_bp_brs
Real number (ℝ≥0)

ZEROS

Distinct335
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.30242947
Minimum0
Maximum671
Zeros2502
Zeros (%)19.6%
Memory size99.8 KiB
2021-03-18T16:43:39.688767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median9
Q328
95-th percentile117
Maximum671
Range671
Interquartile range (IQR)27

Descriptive statistics

Standard deviation48.64663418
Coefficient of variation (CV)1.849511059
Kurtosis21.83584598
Mean26.30242947
Median Absolute Deviation (MAD)9
Skewness3.99843549
Sum335619
Variance2366.495017
MonotocityNot monotonic
2021-03-18T16:43:39.789663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02502
19.6%
1902
 
7.1%
2611
 
4.8%
3479
 
3.8%
4479
 
3.8%
6361
 
2.8%
5351
 
2.8%
9294
 
2.3%
7288
 
2.3%
8288
 
2.3%
Other values (325)6205
48.6%
ValueCountFrequency (%)
02502
19.6%
1902
 
7.1%
2611
 
4.8%
3479
 
3.8%
4479
 
3.8%
ValueCountFrequency (%)
6711
< 0.1%
5131
< 0.1%
4961
< 0.1%
4821
< 0.1%
4752
< 0.1%

nb_voe_pp_at
Real number (ℝ≥0)

ZEROS

Distinct1244
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean222.7919279
Minimum0
Maximum6209
Zeros310
Zeros (%)2.4%
Memory size99.8 KiB
2021-03-18T16:43:39.896348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q137
median95
Q3215
95-th percentile765.15
Maximum6209
Range6209
Interquartile range (IQR)178

Descriptive statistics

Standard deviation463.2967507
Coefficient of variation (CV)2.079504204
Kurtosis43.26186069
Mean222.7919279
Median Absolute Deviation (MAD)71
Skewness5.786423516
Sum2842825
Variance214643.8792
MonotocityNot monotonic
2021-03-18T16:43:39.994712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0310
 
2.4%
16102
 
0.8%
29100
 
0.8%
2099
 
0.8%
1198
 
0.8%
1098
 
0.8%
1795
 
0.7%
1595
 
0.7%
795
 
0.7%
2790
 
0.7%
Other values (1234)11578
90.7%
ValueCountFrequency (%)
0310
2.4%
163
 
0.5%
249
 
0.4%
356
 
0.4%
463
 
0.5%
ValueCountFrequency (%)
62091
< 0.1%
57921
< 0.1%
57711
< 0.1%
57251
< 0.1%
56131
< 0.1%

nb_voe_pc
Real number (ℝ≥0)

ZEROS

Distinct474
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.47821317
Minimum0
Maximum1755
Zeros6284
Zeros (%)49.2%
Memory size99.8 KiB
2021-03-18T16:43:40.098407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q331
95-th percentile165
Maximum1755
Range1755
Interquartile range (IQR)31

Descriptive statistics

Standard deviation90.64231522
Coefficient of variation (CV)2.628973688
Kurtosis73.01150769
Mean34.47821317
Median Absolute Deviation (MAD)1
Skewness6.851433317
Sum439942
Variance8216.029308
MonotocityNot monotonic
2021-03-18T16:43:40.196310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06284
49.2%
1221
 
1.7%
2214
 
1.7%
3182
 
1.4%
5164
 
1.3%
4161
 
1.3%
6150
 
1.2%
7147
 
1.2%
8147
 
1.2%
10143
 
1.1%
Other values (464)4947
38.8%
ValueCountFrequency (%)
06284
49.2%
1221
 
1.7%
2214
 
1.7%
3182
 
1.4%
4161
 
1.3%
ValueCountFrequency (%)
17551
< 0.1%
17361
< 0.1%
16961
< 0.1%
13901
< 0.1%
13161
< 0.1%

nb_voe_pc_bg
Real number (ℝ≥0)

ZEROS

Distinct194
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.985815047
Minimum0
Maximum792
Zeros8338
Zeros (%)65.3%
Memory size99.8 KiB
2021-03-18T16:43:40.301633image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile35
Maximum792
Range792
Interquartile range (IQR)4

Descriptive statistics

Standard deviation25.18760152
Coefficient of variation (CV)3.605535123
Kurtosis197.3207302
Mean6.985815047
Median Absolute Deviation (MAD)0
Skewness11.03607912
Sum89139
Variance634.4152702
MonotocityNot monotonic
2021-03-18T16:43:40.403431image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08338
65.3%
1489
 
3.8%
2380
 
3.0%
3267
 
2.1%
4258
 
2.0%
5250
 
2.0%
7200
 
1.6%
6199
 
1.6%
8153
 
1.2%
10126
 
1.0%
Other values (184)2100
 
16.5%
ValueCountFrequency (%)
08338
65.3%
1489
 
3.8%
2380
 
3.0%
3267
 
2.1%
4258
 
2.0%
ValueCountFrequency (%)
7921
< 0.1%
5411
< 0.1%
5151
< 0.1%
5051
< 0.1%
5031
< 0.1%

nb_voe_pc_bt
Real number (ℝ≥0)

ZEROS

Distinct169
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.537539185
Minimum0
Maximum1090
Zeros8290
Zeros (%)65.0%
Memory size99.8 KiB
2021-03-18T16:43:40.510837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile26
Maximum1090
Range1090
Interquartile range (IQR)3

Descriptive statistics

Standard deviation23.73940215
Coefficient of variation (CV)4.286994883
Kurtosis534.0122007
Mean5.537539185
Median Absolute Deviation (MAD)0
Skewness17.50565729
Sum70659
Variance563.5592146
MonotocityNot monotonic
2021-03-18T16:43:40.613961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08290
65.0%
1662
 
5.2%
2482
 
3.8%
3377
 
3.0%
4306
 
2.4%
5262
 
2.1%
6222
 
1.7%
7192
 
1.5%
10149
 
1.2%
8149
 
1.2%
Other values (159)1669
 
13.1%
ValueCountFrequency (%)
08290
65.0%
1662
 
5.2%
2482
 
3.8%
3377
 
3.0%
4306
 
2.4%
ValueCountFrequency (%)
10901
< 0.1%
7621
< 0.1%
5831
< 0.1%
5351
< 0.1%
4661
< 0.1%

nb_voe_pc_bp
Real number (ℝ≥0)

ZEROS

Distinct134
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.969514107
Minimum0
Maximum543
Zeros8627
Zeros (%)67.6%
Memory size99.8 KiB
2021-03-18T16:43:40.712946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile20
Maximum543
Range543
Interquartile range (IQR)2

Descriptive statistics

Standard deviation15.82665113
Coefficient of variation (CV)3.987049977
Kurtosis331.8311126
Mean3.969514107
Median Absolute Deviation (MAD)0
Skewness14.29104417
Sum50651
Variance250.4828859
MonotocityNot monotonic
2021-03-18T16:43:40.818756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08627
67.6%
1692
 
5.4%
2529
 
4.1%
3397
 
3.1%
4302
 
2.4%
5228
 
1.8%
6221
 
1.7%
8166
 
1.3%
7163
 
1.3%
9114
 
0.9%
Other values (124)1321
 
10.4%
ValueCountFrequency (%)
08627
67.6%
1692
 
5.4%
2529
 
4.1%
3397
 
3.1%
4302
 
2.4%
ValueCountFrequency (%)
5431
< 0.1%
5221
< 0.1%
4201
< 0.1%
3701
< 0.1%
3282
< 0.1%

nb_voe_pc_at
Real number (ℝ≥0)

ZEROS

Distinct318
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.98534483
Minimum0
Maximum979
Zeros6614
Zeros (%)51.8%
Memory size99.8 KiB
2021-03-18T16:43:40.921244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q315
95-th percentile90
Maximum979
Range979
Interquartile range (IQR)15

Descriptive statistics

Standard deviation46.72815046
Coefficient of variation (CV)2.598123689
Kurtosis57.8890773
Mean17.98534483
Median Absolute Deviation (MAD)0
Skewness6.083237157
Sum229493
Variance2183.520045
MonotocityNot monotonic
2021-03-18T16:43:41.022665image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06614
51.8%
1395
 
3.1%
2352
 
2.8%
3278
 
2.2%
4274
 
2.1%
5227
 
1.8%
6207
 
1.6%
7185
 
1.4%
8165
 
1.3%
10158
 
1.2%
Other values (308)3905
30.6%
ValueCountFrequency (%)
06614
51.8%
1395
 
3.1%
2352
 
2.8%
3278
 
2.2%
4274
 
2.1%
ValueCountFrequency (%)
9791
< 0.1%
7391
< 0.1%
6741
< 0.1%
6261
< 0.1%
6061
< 0.1%

nb_cla_pp
Real number (ℝ≥0)

ZEROS

Distinct2368
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean592.0582288
Minimum0
Maximum14968
Zeros234
Zeros (%)1.8%
Memory size99.8 KiB
2021-03-18T16:43:41.123148image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24
Q1113
median247
Q3566
95-th percentile2550.15
Maximum14968
Range14968
Interquartile range (IQR)453

Descriptive statistics

Standard deviation1027.111384
Coefficient of variation (CV)1.734814811
Kurtosis28.62036267
Mean592.0582288
Median Absolute Deviation (MAD)168
Skewness4.40301617
Sum7554663
Variance1054957.795
MonotocityNot monotonic
2021-03-18T16:43:41.513681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0234
 
1.8%
10049
 
0.4%
10744
 
0.3%
15143
 
0.3%
8341
 
0.3%
6041
 
0.3%
7040
 
0.3%
9340
 
0.3%
5040
 
0.3%
13639
 
0.3%
Other values (2358)12149
95.2%
ValueCountFrequency (%)
0234
1.8%
15
 
< 0.1%
210
 
0.1%
313
 
0.1%
414
 
0.1%
ValueCountFrequency (%)
149681
< 0.1%
133651
< 0.1%
132131
< 0.1%
131521
< 0.1%
130611
< 0.1%

nb_cla_pc
Real number (ℝ≥0)

ZEROS

Distinct283
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.73636364
Minimum0
Maximum933
Zeros7073
Zeros (%)55.4%
Memory size99.8 KiB
2021-03-18T16:43:41.622143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37
95-th percentile64
Maximum933
Range933
Interquartile range (IQR)7

Descriptive statistics

Standard deviation42.01345345
Coefficient of variation (CV)3.298700842
Kurtosis113.0958188
Mean12.73636364
Median Absolute Deviation (MAD)0
Skewness8.507749202
Sum162516
Variance1765.130271
MonotocityNot monotonic
2021-03-18T16:43:41.719601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07073
55.4%
1641
 
5.0%
2490
 
3.8%
3398
 
3.1%
4315
 
2.5%
5266
 
2.1%
6243
 
1.9%
7219
 
1.7%
9188
 
1.5%
8184
 
1.4%
Other values (273)2743
 
21.5%
ValueCountFrequency (%)
07073
55.4%
1641
 
5.0%
2490
 
3.8%
3398
 
3.1%
4315
 
2.5%
ValueCountFrequency (%)
9331
< 0.1%
9101
< 0.1%
8911
< 0.1%
8411
< 0.1%
6771
< 0.1%

nb_cla_pp_internat
Real number (ℝ≥0)

MISSING
ZEROS

Distinct222
Distinct (%)26.2%
Missing11913
Missing (%)93.4%
Infinite0
Infinite (%)0.0%
Mean91.13577332
Minimum0
Maximum3733
Zeros198
Zeros (%)1.6%
Memory size99.8 KiB
2021-03-18T16:43:41.818726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median35
Q378
95-th percentile382.4
Maximum3733
Range3733
Interquartile range (IQR)71

Descriptive statistics

Standard deviation245.6561614
Coefficient of variation (CV)2.695496537
Kurtosis95.06811775
Mean91.13577332
Median Absolute Deviation (MAD)35
Skewness8.435669491
Sum77192
Variance60346.94963
MonotocityNot monotonic
2021-03-18T16:43:41.919798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0198
 
1.6%
1014
 
0.1%
2212
 
0.1%
3012
 
0.1%
2611
 
0.1%
4511
 
0.1%
1610
 
0.1%
3310
 
0.1%
3610
 
0.1%
179
 
0.1%
Other values (212)550
 
4.3%
(Missing)11913
93.4%
ValueCountFrequency (%)
0198
1.6%
13
 
< 0.1%
24
 
< 0.1%
42
 
< 0.1%
52
 
< 0.1%
ValueCountFrequency (%)
37331
< 0.1%
27241
< 0.1%
26701
< 0.1%
22451
< 0.1%
12631
< 0.1%

nb_cla_pp_pasinternat
Real number (ℝ≥0)

MISSING

Distinct559
Distinct (%)66.0%
Missing11913
Missing (%)93.4%
Infinite0
Infinite (%)0.0%
Mean514.3671783
Minimum0
Maximum4084
Zeros24
Zeros (%)0.2%
Memory size99.8 KiB
2021-03-18T16:43:42.026073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile71
Q1210
median385
Q3660
95-th percentile1349
Maximum4084
Range4084
Interquartile range (IQR)450

Descriptive statistics

Standard deviation466.7801417
Coefficient of variation (CV)0.9074843059
Kurtosis12.79238257
Mean514.3671783
Median Absolute Deviation (MAD)213
Skewness2.722210457
Sum435669
Variance217883.7007
MonotocityNot monotonic
2021-03-18T16:43:42.130256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
024
 
0.2%
6355
 
< 0.1%
1275
 
< 0.1%
3505
 
< 0.1%
1715
 
< 0.1%
2355
 
< 0.1%
5415
 
< 0.1%
1594
 
< 0.1%
3064
 
< 0.1%
6514
 
< 0.1%
Other values (549)781
 
6.1%
(Missing)11913
93.4%
ValueCountFrequency (%)
024
0.2%
61
 
< 0.1%
351
 
< 0.1%
421
 
< 0.1%
471
 
< 0.1%
ValueCountFrequency (%)
40841
< 0.1%
38621
< 0.1%
38571
< 0.1%
29261
< 0.1%
29091
< 0.1%

nb_cla_pp_bg
Real number (ℝ≥0)

ZEROS

Distinct1734
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean309.6721787
Minimum0
Maximum12340
Zeros765
Zeros (%)6.0%
Memory size99.8 KiB
2021-03-18T16:43:42.235875image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q119
median68
Q3305
95-th percentile1381
Maximum12340
Range12340
Interquartile range (IQR)286

Descriptive statistics

Standard deviation679.8351424
Coefficient of variation (CV)2.195338132
Kurtosis59.17951086
Mean309.6721787
Median Absolute Deviation (MAD)62
Skewness6.011630966
Sum3951417
Variance462175.8208
MonotocityNot monotonic
2021-03-18T16:43:42.339221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0765
 
6.0%
7162
 
1.3%
1151
 
1.2%
13150
 
1.2%
4150
 
1.2%
6143
 
1.1%
12142
 
1.1%
9142
 
1.1%
5141
 
1.1%
10134
 
1.1%
Other values (1724)10680
83.7%
ValueCountFrequency (%)
0765
6.0%
1151
 
1.2%
2126
 
1.0%
3132
 
1.0%
4150
 
1.2%
ValueCountFrequency (%)
123401
< 0.1%
121771
< 0.1%
113081
< 0.1%
109061
< 0.1%
101421
< 0.1%

nb_cla_pp_bg_brs
Real number (ℝ≥0)

ZEROS

Distinct549
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.96551724
Minimum0
Maximum2382
Zeros1505
Zeros (%)11.8%
Memory size99.8 KiB
2021-03-18T16:43:42.438239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median13
Q347
95-th percentile216
Maximum2382
Range2382
Interquartile range (IQR)44

Descriptive statistics

Standard deviation112.1199445
Coefficient of variation (CV)2.243946439
Kurtosis63.38232533
Mean49.96551724
Median Absolute Deviation (MAD)12
Skewness6.3109597
Sum637560
Variance12570.88195
MonotocityNot monotonic
2021-03-18T16:43:42.540383image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01505
 
11.8%
1710
 
5.6%
2663
 
5.2%
3595
 
4.7%
4507
 
4.0%
5447
 
3.5%
6358
 
2.8%
7330
 
2.6%
8320
 
2.5%
9290
 
2.3%
Other values (539)7035
55.1%
ValueCountFrequency (%)
01505
11.8%
1710
5.6%
2663
5.2%
3595
 
4.7%
4507
 
4.0%
ValueCountFrequency (%)
23821
< 0.1%
19891
< 0.1%
16471
< 0.1%
15321
< 0.1%
15181
< 0.1%

nb_cla_pp_bt
Real number (ℝ≥0)

ZEROS

Distinct735
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.34278997
Minimum0
Maximum1855
Zeros1691
Zeros (%)13.3%
Memory size99.8 KiB
2021-03-18T16:43:42.640053image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median45
Q3104
95-th percentile341
Maximum1855
Range1855
Interquartile range (IQR)94

Descriptive statistics

Standard deviation144.0189812
Coefficient of variation (CV)1.611982133
Kurtosis20.30385371
Mean89.34278997
Median Absolute Deviation (MAD)41
Skewness3.866062536
Sum1140014
Variance20741.46694
MonotocityNot monotonic
2021-03-18T16:43:42.740184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01691
 
13.3%
1298
 
2.3%
2197
 
1.5%
3188
 
1.5%
4144
 
1.1%
5138
 
1.1%
6129
 
1.0%
9125
 
1.0%
7124
 
1.0%
8113
 
0.9%
Other values (725)9613
75.3%
ValueCountFrequency (%)
01691
13.3%
1298
 
2.3%
2197
 
1.5%
3188
 
1.5%
4144
 
1.1%
ValueCountFrequency (%)
18551
< 0.1%
16271
< 0.1%
14911
< 0.1%
13881
< 0.1%
13261
< 0.1%

nb_cla_pp_bt_brs
Real number (ℝ≥0)

ZEROS

Distinct319
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.54592476
Minimum0
Maximum693
Zeros2325
Zeros (%)18.2%
Memory size99.8 KiB
2021-03-18T16:43:42.845645image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median10
Q329
95-th percentile102
Maximum693
Range693
Interquartile range (IQR)27

Descriptive statistics

Standard deviation45.85451449
Coefficient of variation (CV)1.794983541
Kurtosis28.60263478
Mean25.54592476
Median Absolute Deviation (MAD)10
Skewness4.439004921
Sum325966
Variance2102.636499
MonotocityNot monotonic
2021-03-18T16:43:42.946101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02325
 
18.2%
1600
 
4.7%
2528
 
4.1%
3468
 
3.7%
4425
 
3.3%
5404
 
3.2%
6395
 
3.1%
7359
 
2.8%
8325
 
2.5%
10298
 
2.3%
Other values (309)6633
52.0%
ValueCountFrequency (%)
02325
18.2%
1600
 
4.7%
2528
 
4.1%
3468
 
3.7%
4425
 
3.3%
ValueCountFrequency (%)
6931
< 0.1%
6001
< 0.1%
5911
< 0.1%
5232
< 0.1%
4941
< 0.1%

nb_cla_pp_bp
Real number (ℝ≥0)

ZEROS

Distinct467
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.07774295
Minimum0
Maximum1446
Zeros2898
Zeros (%)22.7%
Memory size99.8 KiB
2021-03-18T16:43:43.052000image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median16
Q354
95-th percentile178
Maximum1446
Range1446
Interquartile range (IQR)53

Descriptive statistics

Standard deviation78.42378804
Coefficient of variation (CV)1.77921515
Kurtosis27.96357055
Mean44.07774295
Median Absolute Deviation (MAD)16
Skewness4.207856343
Sum562432
Variance6150.290531
MonotocityNot monotonic
2021-03-18T16:43:43.153211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02898
 
22.7%
1579
 
4.5%
2430
 
3.4%
3325
 
2.5%
4252
 
2.0%
5236
 
1.8%
6228
 
1.8%
7223
 
1.7%
8179
 
1.4%
11156
 
1.2%
Other values (457)7254
56.8%
ValueCountFrequency (%)
02898
22.7%
1579
 
4.5%
2430
 
3.4%
3325
 
2.5%
4252
 
2.0%
ValueCountFrequency (%)
14461
< 0.1%
8841
< 0.1%
8291
< 0.1%
8061
< 0.1%
7741
< 0.1%

nb_cla_pp_bp_brs
Real number (ℝ≥0)

ZEROS

Distinct252
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.45501567
Minimum0
Maximum671
Zeros3631
Zeros (%)28.5%
Memory size99.8 KiB
2021-03-18T16:43:43.259888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q319
95-th percentile76
Maximum671
Range671
Interquartile range (IQR)19

Descriptive statistics

Standard deviation34.26010129
Coefficient of variation (CV)1.962765427
Kurtosis35.44796335
Mean17.45501567
Median Absolute Deviation (MAD)5
Skewness4.740143851
Sum222726
Variance1173.75454
MonotocityNot monotonic
2021-03-18T16:43:43.357052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03631
28.5%
1919
 
7.2%
2610
 
4.8%
3488
 
3.8%
4442
 
3.5%
5385
 
3.0%
6347
 
2.7%
7321
 
2.5%
8293
 
2.3%
9286
 
2.2%
Other values (242)5038
39.5%
ValueCountFrequency (%)
03631
28.5%
1919
 
7.2%
2610
 
4.8%
3488
 
3.8%
4442
 
3.5%
ValueCountFrequency (%)
6711
< 0.1%
3841
< 0.1%
3801
< 0.1%
3641
< 0.1%
3631
< 0.1%

nb_cla_pp_at
Real number (ℝ≥0)

ZEROS

Distinct1088
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean148.9655172
Minimum0
Maximum4870
Zeros508
Zeros (%)4.0%
Memory size99.8 KiB
2021-03-18T16:43:43.457913image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q115
median42
Q3114
95-th percentile605.05
Maximum4870
Range4870
Interquartile range (IQR)99

Descriptive statistics

Standard deviation374.3749739
Coefficient of variation (CV)2.513165334
Kurtosis39.7091069
Mean148.9655172
Median Absolute Deviation (MAD)34
Skewness5.666037852
Sum1900800
Variance140156.6211
MonotocityNot monotonic
2021-03-18T16:43:43.559267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0508
 
4.0%
6215
 
1.7%
9212
 
1.7%
1208
 
1.6%
3207
 
1.6%
4201
 
1.6%
2196
 
1.5%
8194
 
1.5%
7186
 
1.5%
5186
 
1.5%
Other values (1078)10447
81.9%
ValueCountFrequency (%)
0508
4.0%
1208
1.6%
2196
 
1.5%
3207
1.6%
4201
 
1.6%
ValueCountFrequency (%)
48701
< 0.1%
45961
< 0.1%
43061
< 0.1%
42511
< 0.1%
42351
< 0.1%

prop_tot
Real number (ℝ≥0)

Distinct1448
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean267.4173197
Minimum0
Maximum5297
Zeros7
Zeros (%)0.1%
Memory size99.8 KiB
2021-03-18T16:43:43.656064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25
Q168
median125
Q3275
95-th percentile1007.05
Maximum5297
Range5297
Interquartile range (IQR)207

Descriptive statistics

Standard deviation411.0673237
Coefficient of variation (CV)1.537175393
Kurtosis24.4610384
Mean267.4173197
Median Absolute Deviation (MAD)72
Skewness4.144200961
Sum3412245
Variance168976.3446
MonotocityNot monotonic
2021-03-18T16:43:43.758281image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8878
 
0.6%
9875
 
0.6%
5374
 
0.6%
6073
 
0.6%
7173
 
0.6%
6773
 
0.6%
5273
 
0.6%
6472
 
0.6%
6872
 
0.6%
8671
 
0.6%
Other values (1438)12026
94.2%
ValueCountFrequency (%)
07
 
0.1%
16
 
< 0.1%
211
0.1%
318
0.1%
426
0.2%
ValueCountFrequency (%)
52971
< 0.1%
52941
< 0.1%
46571
< 0.1%
45951
< 0.1%
44871
< 0.1%

acc_tot
Real number (ℝ≥0)

Distinct492
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.79717868
Minimum0
Maximum1589
Zeros65
Zeros (%)0.5%
Memory size99.8 KiB
2021-03-18T16:43:43.859460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q117
median29
Q350
95-th percentile177
Maximum1589
Range1589
Interquartile range (IQR)33

Descriptive statistics

Standard deviation86.91621624
Coefficient of variation (CV)1.646228424
Kurtosis60.79662921
Mean52.79717868
Median Absolute Deviation (MAD)14
Skewness6.263842864
Sum673692
Variance7554.428646
MonotocityNot monotonic
2021-03-18T16:43:43.961664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15467
 
3.7%
24343
 
2.7%
14330
 
2.6%
23316
 
2.5%
34313
 
2.5%
30310
 
2.4%
28297
 
2.3%
12290
 
2.3%
33288
 
2.3%
22286
 
2.2%
Other values (482)9520
74.6%
ValueCountFrequency (%)
065
0.5%
173
0.6%
281
0.6%
3106
0.8%
484
0.7%
ValueCountFrequency (%)
15891
< 0.1%
15261
< 0.1%
14581
< 0.1%
13711
< 0.1%
12651
< 0.1%

acc_tot_f
Real number (ℝ≥0)

ZEROS

Distinct353
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.3523511
Minimum0
Maximum1109
Zeros1063
Zeros (%)8.3%
Memory size99.8 KiB
2021-03-18T16:43:44.071464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median15
Q329
95-th percentile107
Maximum1109
Range1109
Interquartile range (IQR)24

Descriptive statistics

Standard deviation58.16416133
Coefficient of variation (CV)1.98158441
Kurtosis80.93177728
Mean29.3523511
Median Absolute Deviation (MAD)11
Skewness7.267938021
Sum374536
Variance3383.069663
MonotocityNot monotonic
2021-03-18T16:43:44.172044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01063
 
8.3%
1675
 
5.3%
2503
 
3.9%
3419
 
3.3%
5367
 
2.9%
12358
 
2.8%
4347
 
2.7%
8347
 
2.7%
13338
 
2.6%
9332
 
2.6%
Other values (343)8011
62.8%
ValueCountFrequency (%)
01063
8.3%
1675
5.3%
2503
3.9%
3419
 
3.3%
4347
 
2.7%
ValueCountFrequency (%)
11091
< 0.1%
10221
< 0.1%
9751
< 0.1%
9691
< 0.1%
9571
< 0.1%

acc_pp
Real number (ℝ≥0)

ZEROS

Distinct456
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.6700627
Minimum0
Maximum1530
Zeros312
Zeros (%)2.4%
Memory size99.8 KiB
2021-03-18T16:43:44.275164image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q115
median26
Q345
95-th percentile159
Maximum1530
Range1530
Interquartile range (IQR)30

Descriptive statistics

Standard deviation82.36141603
Coefficient of variation (CV)1.727738781
Kurtosis69.6383277
Mean47.6700627
Median Absolute Deviation (MAD)13
Skewness6.725609316
Sum608270
Variance6783.40285
MonotocityNot monotonic
2021-03-18T16:43:44.379127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15414
 
3.2%
14324
 
2.5%
23313
 
2.5%
0312
 
2.4%
24310
 
2.4%
12307
 
2.4%
16292
 
2.3%
13290
 
2.3%
11284
 
2.2%
30284
 
2.2%
Other values (446)9630
75.5%
ValueCountFrequency (%)
0312
2.4%
193
 
0.7%
2117
 
0.9%
3139
1.1%
4136
1.1%
ValueCountFrequency (%)
15301
< 0.1%
15261
< 0.1%
13711
< 0.1%
13251
< 0.1%
12641
< 0.1%

acc_pc
Real number (ℝ≥0)

ZEROS

Distinct151
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.731739812
Minimum0
Maximum350
Zeros7669
Zeros (%)60.1%
Memory size99.8 KiB
2021-03-18T16:43:44.522776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile24
Maximum350
Range350
Interquartile range (IQR)3

Descriptive statistics

Standard deviation15.35877782
Coefficient of variation (CV)3.245904981
Kurtosis108.0100806
Mean4.731739812
Median Absolute Deviation (MAD)0
Skewness8.344885738
Sum60377
Variance235.8920562
MonotocityNot monotonic
2021-03-18T16:43:44.666120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07669
60.1%
1972
 
7.6%
2672
 
5.3%
3501
 
3.9%
4393
 
3.1%
5291
 
2.3%
6244
 
1.9%
7200
 
1.6%
8185
 
1.4%
9136
 
1.1%
Other values (141)1497
 
11.7%
ValueCountFrequency (%)
07669
60.1%
1972
 
7.6%
2672
 
5.3%
3501
 
3.9%
4393
 
3.1%
ValueCountFrequency (%)
3501
< 0.1%
3441
< 0.1%
2771
< 0.1%
2761
< 0.1%
2731
< 0.1%

acc_debutpp
Real number (ℝ≥0)

ZEROS

Distinct256
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.18119122
Minimum0
Maximum887
Zeros1104
Zeros (%)8.7%
Memory size99.8 KiB
2021-03-18T16:43:45.193407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q312
95-th percentile55
Maximum887
Range887
Interquartile range (IQR)10

Descriptive statistics

Standard deviation33.56927986
Coefficient of variation (CV)2.367169255
Kurtosis107.3795917
Mean14.18119122
Median Absolute Deviation (MAD)4
Skewness8.211550293
Sum180952
Variance1126.89655
MonotocityNot monotonic
2021-03-18T16:43:45.299783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31183
 
9.3%
21140
 
8.9%
01104
 
8.7%
11024
 
8.0%
4997
 
7.8%
5913
 
7.2%
6744
 
5.8%
7638
 
5.0%
8536
 
4.2%
9466
 
3.7%
Other values (246)4015
31.5%
ValueCountFrequency (%)
01104
8.7%
11024
8.0%
21140
8.9%
31183
9.3%
4997
7.8%
ValueCountFrequency (%)
8871
< 0.1%
6411
< 0.1%
5861
< 0.1%
5651
< 0.1%
5321
< 0.1%

acc_datebac
Real number (ℝ≥0)

ZEROS

Distinct420
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.45250784
Minimum0
Maximum1526
Zeros336
Zeros (%)2.6%
Memory size99.8 KiB
2021-03-18T16:43:45.397547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q112
median22
Q337
95-th percentile138
Maximum1526
Range1526
Interquartile range (IQR)25

Descriptive statistics

Standard deviation71.77621038
Coefficient of variation (CV)1.774332772
Kurtosis79.62438002
Mean40.45250784
Median Absolute Deviation (MAD)11
Skewness7.076355872
Sum516174
Variance5151.824376
MonotocityNot monotonic
2021-03-18T16:43:45.495904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12385
 
3.0%
13378
 
3.0%
14378
 
3.0%
10365
 
2.9%
17365
 
2.9%
19360
 
2.8%
11359
 
2.8%
0336
 
2.6%
21333
 
2.6%
9327
 
2.6%
Other values (410)9174
71.9%
ValueCountFrequency (%)
0336
2.6%
1108
 
0.8%
2127
 
1.0%
3163
1.3%
4169
1.3%
ValueCountFrequency (%)
15261
< 0.1%
14091
< 0.1%
12151
< 0.1%
11801
< 0.1%
10951
< 0.1%

acc_finpp
Real number (ℝ≥0)

ZEROS

Distinct448
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.76159875
Minimum0
Maximum1526
Zeros188
Zeros (%)1.5%
Memory size99.8 KiB
2021-03-18T16:43:45.602721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q115
median25
Q345
95-th percentile157
Maximum1526
Range1526
Interquartile range (IQR)30

Descriptive statistics

Standard deviation78.7106961
Coefficient of variation (CV)1.683233641
Kurtosis67.51636151
Mean46.76159875
Median Absolute Deviation (MAD)12
Skewness6.554326652
Sum596678
Variance6195.37368
MonotocityNot monotonic
2021-03-18T16:43:45.714581image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14383
 
3.0%
12375
 
2.9%
13350
 
2.7%
15326
 
2.6%
23315
 
2.5%
20313
 
2.5%
21307
 
2.4%
25306
 
2.4%
22304
 
2.4%
26301
 
2.4%
Other values (438)9480
74.3%
ValueCountFrequency (%)
0188
1.5%
188
0.7%
2102
0.8%
3123
1.0%
4125
1.0%
ValueCountFrequency (%)
15261
< 0.1%
14381
< 0.1%
13091
< 0.1%
12851
< 0.1%
11581
< 0.1%

acc_internat
Real number (ℝ≥0)

MISSING
ZEROS

Distinct65
Distinct (%)7.7%
Missing11913
Missing (%)93.4%
Infinite0
Infinite (%)0.0%
Mean12.3081464
Minimum0
Maximum141
Zeros210
Zeros (%)1.6%
Memory size99.8 KiB
2021-03-18T16:43:45.828017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q318
95-th percentile40
Maximum141
Range141
Interquartile range (IQR)17

Descriptive statistics

Standard deviation15.37877071
Coefficient of variation (CV)1.24947902
Kurtosis12.5164584
Mean12.3081464
Median Absolute Deviation (MAD)8
Skewness2.684902503
Sum10425
Variance236.5065884
MonotocityNot monotonic
2021-03-18T16:43:45.924492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0210
 
1.6%
239
 
0.3%
433
 
0.3%
331
 
0.2%
527
 
0.2%
127
 
0.2%
826
 
0.2%
1726
 
0.2%
926
 
0.2%
625
 
0.2%
Other values (55)377
 
3.0%
(Missing)11913
93.4%
ValueCountFrequency (%)
0210
1.6%
127
 
0.2%
239
 
0.3%
331
 
0.2%
433
 
0.3%
ValueCountFrequency (%)
1411
< 0.1%
1251
< 0.1%
1071
< 0.1%
851
< 0.1%
801
< 0.1%

acc_brs
Real number (ℝ≥0)

ZEROS

Distinct162
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.911677116
Minimum0
Maximum341
Zeros976
Zeros (%)7.6%
Memory size99.8 KiB
2021-03-18T16:43:46.028088image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q311
95-th percentile33
Maximum341
Range341
Interquartile range (IQR)9

Descriptive statistics

Standard deviation17.87246899
Coefficient of variation (CV)1.803173043
Kurtosis74.89686467
Mean9.911677116
Median Absolute Deviation (MAD)3
Skewness7.033368014
Sum126473
Variance319.4251477
MonotocityNot monotonic
2021-03-18T16:43:46.129206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21190
 
9.3%
11150
 
9.0%
31122
 
8.8%
41085
 
8.5%
0976
 
7.6%
5927
 
7.3%
6795
 
6.2%
7716
 
5.6%
8620
 
4.9%
9521
 
4.1%
Other values (152)3658
28.7%
ValueCountFrequency (%)
0976
7.6%
11150
9.0%
21190
9.3%
31122
8.8%
41085
8.5%
ValueCountFrequency (%)
3411
< 0.1%
3221
< 0.1%
2852
< 0.1%
2781
< 0.1%
2711
< 0.1%

acc_neobac
Real number (ℝ≥0)

ZEROS

Distinct409
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.93048589
Minimum0
Maximum1401
Zeros159
Zeros (%)1.2%
Memory size99.8 KiB
2021-03-18T16:43:46.233824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q113
median22
Q338
95-th percentile135
Maximum1401
Range1401
Interquartile range (IQR)25

Descriptive statistics

Standard deviation69.51648723
Coefficient of variation (CV)1.740937674
Kurtosis79.97647174
Mean39.93048589
Median Absolute Deviation (MAD)11
Skewness7.159443518
Sum509513
Variance4832.541997
MonotocityNot monotonic
2021-03-18T16:43:46.335853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14426
 
3.3%
11394
 
3.1%
13386
 
3.0%
10373
 
2.9%
15349
 
2.7%
22348
 
2.7%
12348
 
2.7%
16326
 
2.6%
20321
 
2.5%
9320
 
2.5%
Other values (399)9169
71.9%
ValueCountFrequency (%)
0159
1.2%
1129
1.0%
2161
1.3%
3161
1.3%
4174
1.4%
ValueCountFrequency (%)
14011
< 0.1%
12691
< 0.1%
12461
< 0.1%
11511
< 0.1%
11311
< 0.1%

acc_bg
Real number (ℝ≥0)

ZEROS

Distinct371
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.63205329
Minimum0
Maximum1401
Zeros1673
Zeros (%)13.1%
Memory size99.8 KiB
2021-03-18T16:43:46.441351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median7
Q326
95-th percentile106.05
Maximum1401
Range1401
Interquartile range (IQR)24

Descriptive statistics

Standard deviation63.40177113
Coefficient of variation (CV)2.380656513
Kurtosis92.27085871
Mean26.63205329
Median Absolute Deviation (MAD)7
Skewness7.65269219
Sum339825
Variance4019.784583
MonotocityNot monotonic
2021-03-18T16:43:46.539660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01673
 
13.1%
11113
 
8.7%
2974
 
7.6%
3823
 
6.4%
4668
 
5.2%
5561
 
4.4%
6477
 
3.7%
7391
 
3.1%
8319
 
2.5%
9293
 
2.3%
Other values (361)5468
42.9%
ValueCountFrequency (%)
01673
13.1%
11113
8.7%
2974
7.6%
3823
6.4%
4668
 
5.2%
ValueCountFrequency (%)
14011
< 0.1%
12071
< 0.1%
10881
< 0.1%
10591
< 0.1%
10461
< 0.1%

acc_bt
Real number (ℝ≥0)

ZEROS

Distinct106
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.313636364
Minimum0
Maximum173
Zeros2601
Zeros (%)20.4%
Memory size99.8 KiB
2021-03-18T16:43:46.651520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q311
95-th percentile28
Maximum173
Range173
Interquartile range (IQR)10

Descriptive statistics

Standard deviation11.33109303
Coefficient of variation (CV)1.362952689
Kurtosis31.78889973
Mean8.313636364
Median Absolute Deviation (MAD)5
Skewness4.161366141
Sum106082
Variance128.3936694
MonotocityNot monotonic
2021-03-18T16:43:46.787456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02601
20.4%
1852
 
6.7%
2801
 
6.3%
3763
 
6.0%
5739
 
5.8%
4698
 
5.5%
7669
 
5.2%
6666
 
5.2%
8589
 
4.6%
9513
 
4.0%
Other values (96)3869
30.3%
ValueCountFrequency (%)
02601
20.4%
1852
 
6.7%
2801
 
6.3%
3763
 
6.0%
4698
 
5.5%
ValueCountFrequency (%)
1731
< 0.1%
1711
< 0.1%
1691
< 0.1%
1661
< 0.1%
1491
< 0.1%

acc_bp
Real number (ℝ≥0)

ZEROS

Distinct62
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.984796238
Minimum0
Maximum128
Zeros4316
Zeros (%)33.8%
Memory size99.8 KiB
2021-03-18T16:43:46.932172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q38
95-th percentile16
Maximum128
Range128
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.568533067
Coefficient of variation (CV)1.317713454
Kurtosis34.05131166
Mean4.984796238
Median Absolute Deviation (MAD)3
Skewness3.551023487
Sum63606
Variance43.14562665
MonotocityNot monotonic
2021-03-18T16:43:47.068614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04316
33.8%
11105
 
8.7%
2752
 
5.9%
3714
 
5.6%
5637
 
5.0%
6636
 
5.0%
4629
 
4.9%
7587
 
4.6%
8525
 
4.1%
9422
 
3.3%
Other values (52)2437
19.1%
ValueCountFrequency (%)
04316
33.8%
11105
 
8.7%
2752
 
5.9%
3714
 
5.6%
4629
 
4.9%
ValueCountFrequency (%)
1281
< 0.1%
1121
< 0.1%
1071
< 0.1%
931
< 0.1%
902
< 0.1%

acc_at
Real number (ℝ≥0)

ZEROS

Distinct215
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.8653605
Minimum0
Maximum482
Zeros1405
Zeros (%)11.0%
Memory size99.8 KiB
2021-03-18T16:43:47.171347image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q312
95-th percentile52
Maximum482
Range482
Interquartile range (IQR)10

Descriptive statistics

Standard deviation25.58941161
Coefficient of variation (CV)1.98901629
Kurtosis59.43224432
Mean12.8653605
Median Absolute Deviation (MAD)4
Skewness6.071916694
Sum164162
Variance654.8179865
MonotocityNot monotonic
2021-03-18T16:43:47.274427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01405
 
11.0%
11261
 
9.9%
21161
 
9.1%
31085
 
8.5%
4940
 
7.4%
5806
 
6.3%
6674
 
5.3%
7587
 
4.6%
8488
 
3.8%
9390
 
3.1%
Other values (205)3963
31.1%
ValueCountFrequency (%)
01405
11.0%
11261
9.9%
21161
9.1%
31085
8.5%
4940
7.4%
ValueCountFrequency (%)
4821
< 0.1%
4781
< 0.1%
4671
< 0.1%
3881
< 0.1%
3551
< 0.1%

acc_mention_nonrenseignee
Real number (ℝ≥0)

ZEROS

Distinct16
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1851880878
Minimum0
Maximum26
Zeros11375
Zeros (%)89.1%
Memory size99.8 KiB
2021-03-18T16:43:47.356795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7657431754
Coefficient of variation (CV)4.134948336
Kurtosis189.9902371
Mean0.1851880878
Median Absolute Deviation (MAD)0
Skewness10.09745718
Sum2363
Variance0.5863626106
MonotocityNot monotonic
2021-03-18T16:43:47.433835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
011375
89.1%
1958
 
7.5%
2219
 
1.7%
393
 
0.7%
447
 
0.4%
521
 
0.2%
715
 
0.1%
615
 
0.1%
114
 
< 0.1%
104
 
< 0.1%
Other values (6)9
 
0.1%
ValueCountFrequency (%)
011375
89.1%
1958
 
7.5%
2219
 
1.7%
393
 
0.7%
447
 
0.4%
ValueCountFrequency (%)
261
 
< 0.1%
181
 
< 0.1%
171
 
< 0.1%
131
 
< 0.1%
114
< 0.1%

acc_sansmention
Real number (ℝ≥0)

ZEROS

Distinct209
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.02413793
Minimum0
Maximum423
Zeros2083
Zeros (%)16.3%
Memory size99.8 KiB
2021-03-18T16:43:47.526775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q310
95-th percentile43
Maximum423
Range423
Interquartile range (IQR)8

Descriptive statistics

Standard deviation23.19105666
Coefficient of variation (CV)2.103661693
Kurtosis54.95869584
Mean11.02413793
Median Absolute Deviation (MAD)4
Skewness6.238320466
Sum140668
Variance537.825109
MonotocityNot monotonic
2021-03-18T16:43:47.622422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02083
16.3%
11033
 
8.1%
2920
 
7.2%
4865
 
6.8%
3862
 
6.8%
5817
 
6.4%
6790
 
6.2%
7702
 
5.5%
8607
 
4.8%
9567
 
4.4%
Other values (199)3514
27.5%
ValueCountFrequency (%)
02083
16.3%
11033
8.1%
2920
7.2%
3862
6.8%
4865
6.8%
ValueCountFrequency (%)
4231
< 0.1%
3551
< 0.1%
3331
< 0.1%
3081
< 0.1%
3071
< 0.1%

acc_ab
Real number (ℝ≥0)

ZEROS

Distinct203
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.10830721
Minimum0
Maximum477
Zeros1023
Zeros (%)8.0%
Memory size99.8 KiB
2021-03-18T16:43:47.718019image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median7
Q313
95-th percentile44
Maximum477
Range477
Interquartile range (IQR)10

Descriptive statistics

Standard deviation23.5195768
Coefficient of variation (CV)1.79424974
Kurtosis69.92142192
Mean13.10830721
Median Absolute Deviation (MAD)5
Skewness6.69747065
Sum167262
Variance553.1704929
MonotocityNot monotonic
2021-03-18T16:43:47.826527image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01023
 
8.0%
5855
 
6.7%
4827
 
6.5%
3822
 
6.4%
6775
 
6.1%
1766
 
6.0%
2753
 
5.9%
7729
 
5.7%
8686
 
5.4%
9570
 
4.5%
Other values (193)4954
38.8%
ValueCountFrequency (%)
01023
8.0%
1766
6.0%
2753
5.9%
3822
6.4%
4827
6.5%
ValueCountFrequency (%)
4771
< 0.1%
4161
< 0.1%
4151
< 0.1%
3751
< 0.1%
3651
< 0.1%

acc_b
Real number (ℝ≥0)

ZEROS

Distinct171
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.775548589
Minimum0
Maximum597
Zeros1419
Zeros (%)11.1%
Memory size99.8 KiB
2021-03-18T16:43:47.929643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q310
95-th percentile36
Maximum597
Range597
Interquartile range (IQR)8

Descriptive statistics

Standard deviation20.16709563
Coefficient of variation (CV)2.063014208
Kurtosis136.9797403
Mean9.775548589
Median Absolute Deviation (MAD)4
Skewness8.920154001
Sum124736
Variance406.7117463
MonotocityNot monotonic
2021-03-18T16:43:48.026934image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01419
11.1%
11379
10.8%
21290
 
10.1%
31209
 
9.5%
41037
 
8.1%
5856
 
6.7%
6723
 
5.7%
7604
 
4.7%
8482
 
3.8%
9429
 
3.4%
Other values (161)3332
26.1%
ValueCountFrequency (%)
01419
11.1%
11379
10.8%
21290
10.1%
31209
9.5%
41037
8.1%
ValueCountFrequency (%)
5971
< 0.1%
3991
< 0.1%
3801
< 0.1%
3621
< 0.1%
3541
< 0.1%

acc_tb
Real number (ℝ≥0)

ZEROS

Distinct167
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.837225705
Minimum0
Maximum865
Zeros4708
Zeros (%)36.9%
Memory size99.8 KiB
2021-03-18T16:43:48.132671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile25
Maximum865
Range865
Interquartile range (IQR)4

Descriptive statistics

Standard deviation21.6091671
Coefficient of variation (CV)3.701958464
Kurtosis431.4122032
Mean5.837225705
Median Absolute Deviation (MAD)1
Skewness16.10570188
Sum74483
Variance466.956103
MonotocityNot monotonic
2021-03-18T16:43:48.233216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04708
36.9%
12444
19.2%
21371
 
10.7%
3825
 
6.5%
4584
 
4.6%
5382
 
3.0%
6307
 
2.4%
7216
 
1.7%
8191
 
1.5%
9146
 
1.1%
Other values (157)1586
 
12.4%
ValueCountFrequency (%)
04708
36.9%
12444
19.2%
21371
 
10.7%
3825
 
6.5%
4584
 
4.6%
ValueCountFrequency (%)
8651
< 0.1%
6431
< 0.1%
6271
< 0.1%
6241
< 0.1%
5081
< 0.1%

acc_bg_mention
Real number (ℝ≥0)

ZEROS

Distinct310
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.52288401
Minimum0
Maximum1386
Zeros3184
Zeros (%)25.0%
Memory size99.8 KiB
2021-03-18T16:43:48.347783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q319
95-th percentile85
Maximum1386
Range1386
Interquartile range (IQR)18

Descriptive statistics

Standard deviation50.92317689
Coefficient of variation (CV)2.608383928
Kurtosis133.5134732
Mean19.52288401
Median Absolute Deviation (MAD)4
Skewness9.037095876
Sum249112
Variance2593.169944
MonotocityNot monotonic
2021-03-18T16:43:48.485578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03184
25.0%
11454
 
11.4%
2906
 
7.1%
3660
 
5.2%
4543
 
4.3%
5402
 
3.2%
6312
 
2.4%
7285
 
2.2%
8234
 
1.8%
10216
 
1.7%
Other values (300)4564
35.8%
ValueCountFrequency (%)
03184
25.0%
11454
11.4%
2906
 
7.1%
3660
 
5.2%
4543
 
4.3%
ValueCountFrequency (%)
13861
< 0.1%
10391
< 0.1%
9831
< 0.1%
9641
< 0.1%
9131
< 0.1%

acc_bt_mention
Real number (ℝ≥0)

ZEROS

Distinct82
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.385423197
Minimum0
Maximum124
Zeros3569
Zeros (%)28.0%
Memory size99.8 KiB
2021-03-18T16:43:48.625359image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q37
95-th percentile20
Maximum124
Range124
Interquartile range (IQR)7

Descriptive statistics

Standard deviation8.329282605
Coefficient of variation (CV)1.54663474
Kurtosis25.54189251
Mean5.385423197
Median Absolute Deviation (MAD)3
Skewness3.909323029
Sum68718
Variance69.37694872
MonotocityNot monotonic
2021-03-18T16:43:48.759759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03569
28.0%
11422
 
11.1%
21207
 
9.5%
3969
 
7.6%
4858
 
6.7%
5729
 
5.7%
6560
 
4.4%
7509
 
4.0%
8410
 
3.2%
9336
 
2.6%
Other values (72)2191
17.2%
ValueCountFrequency (%)
03569
28.0%
11422
 
11.1%
21207
 
9.5%
3969
 
7.6%
4858
 
6.7%
ValueCountFrequency (%)
1241
< 0.1%
1191
< 0.1%
1031
< 0.1%
1011
< 0.1%
981
< 0.1%

acc_bp_mention
Real number (ℝ≥0)

ZEROS

Distinct49
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.812774295
Minimum0
Maximum66
Zeros4761
Zeros (%)37.3%
Memory size99.8 KiB
2021-03-18T16:43:48.862628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q36
95-th percentile14
Maximum66
Range66
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.095065514
Coefficient of variation (CV)1.336314484
Kurtosis12.14424782
Mean3.812774295
Median Absolute Deviation (MAD)2
Skewness2.450489081
Sum48651
Variance25.95969259
MonotocityNot monotonic
2021-03-18T16:43:48.960024image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
04761
37.3%
11293
 
10.1%
2911
 
7.1%
3873
 
6.8%
4737
 
5.8%
5658
 
5.2%
6612
 
4.8%
7550
 
4.3%
8446
 
3.5%
9356
 
2.8%
Other values (39)1563
 
12.2%
ValueCountFrequency (%)
04761
37.3%
11293
 
10.1%
2911
 
7.1%
3873
 
6.8%
4737
 
5.8%
ValueCountFrequency (%)
661
< 0.1%
651
< 0.1%
621
< 0.1%
581
< 0.1%
551
< 0.1%

acc_term
Real number (ℝ≥0)

MISSING
ZEROS

Distinct39
Distinct (%)0.6%
Missing6740
Missing (%)52.8%
Infinite0
Infinite (%)0.0%
Mean4.650996678
Minimum0
Maximum50
Zeros962
Zeros (%)7.5%
Memory size99.8 KiB
2021-03-18T16:43:49.057043image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q37
95-th percentile13
Maximum50
Range50
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.751539725
Coefficient of variation (CV)1.021617527
Kurtosis9.251173682
Mean4.650996678
Median Absolute Deviation (MAD)2
Skewness2.243666403
Sum27999
Variance22.57712976
MonotocityNot monotonic
2021-03-18T16:43:49.161446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0962
 
7.5%
2743
 
5.8%
1686
 
5.4%
3634
 
5.0%
4577
 
4.5%
5482
 
3.8%
6378
 
3.0%
7339
 
2.7%
8260
 
2.0%
9202
 
1.6%
Other values (29)757
 
5.9%
(Missing)6740
52.8%
ValueCountFrequency (%)
0962
7.5%
1686
5.4%
2743
5.8%
3634
5.0%
4577
4.5%
ValueCountFrequency (%)
501
< 0.1%
452
< 0.1%
402
< 0.1%
391
< 0.1%
381
< 0.1%

acc_term_f
Real number (ℝ≥0)

MISSING
ZEROS

Distinct24
Distinct (%)0.4%
Missing6740
Missing (%)52.8%
Infinite0
Infinite (%)0.0%
Mean1.762126246
Minimum0
Maximum25
Zeros2739
Zeros (%)21.5%
Memory size99.8 KiB
2021-03-18T16:43:49.250170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile7
Maximum25
Range25
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.645951143
Coefficient of variation (CV)1.501567296
Kurtosis9.908013141
Mean1.762126246
Median Absolute Deviation (MAD)1
Skewness2.585557949
Sum10608
Variance7.001057449
MonotocityNot monotonic
2021-03-18T16:43:49.337849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
02739
21.5%
11050
 
8.2%
2751
 
5.9%
3423
 
3.3%
4298
 
2.3%
5217
 
1.7%
6181
 
1.4%
7122
 
1.0%
871
 
0.6%
944
 
0.3%
Other values (14)124
 
1.0%
(Missing)6740
52.8%
ValueCountFrequency (%)
02739
21.5%
11050
 
8.2%
2751
 
5.9%
3423
 
3.3%
4298
 
2.3%
ValueCountFrequency (%)
251
 
< 0.1%
241
 
< 0.1%
224
< 0.1%
211
 
< 0.1%
192
< 0.1%

acc_aca_orig
Real number (ℝ≥0)

ZEROS

Distinct340
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.72304075
Minimum0
Maximum1032
Zeros363
Zeros (%)2.8%
Memory size99.8 KiB
2021-03-18T16:43:49.957200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q18
median16
Q328
95-th percentile94
Maximum1032
Range1032
Interquartile range (IQR)20

Descriptive statistics

Standard deviation52.77724427
Coefficient of variation (CV)1.837453239
Kurtosis88.77846374
Mean28.72304075
Median Absolute Deviation (MAD)9
Skewness7.65115021
Sum366506
Variance2785.437513
MonotocityNot monotonic
2021-03-18T16:43:50.110044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10421
 
3.3%
6415
 
3.3%
8411
 
3.2%
9403
 
3.2%
13395
 
3.1%
14388
 
3.0%
12387
 
3.0%
4387
 
3.0%
11385
 
3.0%
15378
 
3.0%
Other values (330)8790
68.9%
ValueCountFrequency (%)
0363
2.8%
1329
2.6%
2347
2.7%
3339
2.7%
4387
3.0%
ValueCountFrequency (%)
10321
< 0.1%
10311
< 0.1%
10171
< 0.1%
9961
< 0.1%
8751
< 0.1%

acc_aca_orig_idf
Real number (ℝ≥0)

ZEROS

Distinct363
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.51653605
Minimum0
Maximum1218
Zeros269
Zeros (%)2.1%
Memory size99.8 KiB
2021-03-18T16:43:50.250173image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q19
median18
Q330
95-th percentile103
Maximum1218
Range1218
Interquartile range (IQR)21

Descriptive statistics

Standard deviation58.10641847
Coefficient of variation (CV)1.843680358
Kurtosis89.95031522
Mean31.51653605
Median Absolute Deviation (MAD)10
Skewness7.672649169
Sum402151
Variance3376.355867
MonotocityNot monotonic
2021-03-18T16:43:50.359246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10422
 
3.3%
14398
 
3.1%
9394
 
3.1%
13393
 
3.1%
8386
 
3.0%
6385
 
3.0%
15384
 
3.0%
11382
 
3.0%
12376
 
2.9%
7360
 
2.8%
Other values (353)8880
69.6%
ValueCountFrequency (%)
0269
2.1%
1258
2.0%
2288
2.3%
3291
2.3%
4331
2.6%
ValueCountFrequency (%)
12181
< 0.1%
10801
< 0.1%
10321
< 0.1%
10311
< 0.1%
10171
< 0.1%

pct_acc_debutpp
Real number (ℝ≥0)

ZEROS

Distinct2189
Distinct (%)17.2%
Missing65
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean26.03759118
Minimum0
Maximum100
Zeros1039
Zeros (%)8.1%
Memory size99.8 KiB
2021-03-18T16:43:50.475692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19.68
median20.59
Q337.095
95-th percentile70.09
Maximum100
Range100
Interquartile range (IQR)27.415

Descriptive statistics

Standard deviation21.7962362
Coefficient of variation (CV)0.8371064761
Kurtosis1.136013696
Mean26.03759118
Median Absolute Deviation (MAD)12.74
Skewness1.164053029
Sum330547.22
Variance475.0759124
MonotocityNot monotonic
2021-03-18T16:43:50.607663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01039
 
8.1%
20261
 
2.0%
33.33261
 
2.0%
25243
 
1.9%
50201
 
1.6%
16.67184
 
1.4%
14.29182
 
1.4%
12.5157
 
1.2%
100133
 
1.0%
40133
 
1.0%
Other values (2179)9901
77.6%
ValueCountFrequency (%)
01039
8.1%
0.441
 
< 0.1%
0.471
 
< 0.1%
0.761
 
< 0.1%
0.831
 
< 0.1%
ValueCountFrequency (%)
100133
1.0%
99.691
 
< 0.1%
99.291
 
< 0.1%
99.151
 
< 0.1%
99.121
 
< 0.1%

pct_acc_datebac
Real number (ℝ≥0)

ZEROS

Distinct2094
Distinct (%)16.5%
Missing65
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean75.13502796
Minimum0
Maximum100
Zeros271
Zeros (%)2.1%
Memory size99.8 KiB
2021-03-18T16:43:50.733682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34.183
Q166.175
median80
Q390
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)23.825

Descriptive statistics

Standard deviation21.0249841
Coefficient of variation (CV)0.2798293242
Kurtosis2.341840045
Mean75.13502796
Median Absolute Deviation (MAD)11.67
Skewness-1.406889881
Sum953839.18
Variance442.0499565
MonotocityNot monotonic
2021-03-18T16:43:50.851063image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1001218
 
9.5%
75277
 
2.2%
0271
 
2.1%
80264
 
2.1%
66.67253
 
2.0%
83.33226
 
1.8%
85.71180
 
1.4%
50174
 
1.4%
87.5158
 
1.2%
86.67131
 
1.0%
Other values (2084)9543
74.8%
ValueCountFrequency (%)
0271
2.1%
3.131
 
< 0.1%
3.853
 
< 0.1%
41
 
< 0.1%
4.171
 
< 0.1%
ValueCountFrequency (%)
1001218
9.5%
99.91
 
< 0.1%
99.712
 
< 0.1%
99.691
 
< 0.1%
99.661
 
< 0.1%

pct_acc_finpp
Real number (ℝ≥0)

Distinct1618
Distinct (%)12.7%
Missing65
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean87.77335014
Minimum0
Maximum100
Zeros123
Zeros (%)1.0%
Memory size99.8 KiB
2021-03-18T16:43:50.955158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile62.833
Q183.33
median91.67
Q398.04
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)14.71

Descriptive statistics

Standard deviation15.11520934
Coefficient of variation (CV)0.1722072738
Kurtosis12.08485258
Mean87.77335014
Median Absolute Deviation (MAD)7.89
Skewness-2.897446321
Sum1114282.68
Variance228.4695533
MonotocityNot monotonic
2021-03-18T16:43:51.060191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1002963
 
23.2%
93.33198
 
1.6%
80191
 
1.5%
85.71187
 
1.5%
91.67173
 
1.4%
87.5170
 
1.3%
90.91166
 
1.3%
83.33165
 
1.3%
90157
 
1.2%
75148
 
1.2%
Other values (1608)8177
64.1%
ValueCountFrequency (%)
0123
1.0%
3.851
 
< 0.1%
4.762
 
< 0.1%
5.561
 
< 0.1%
5.882
 
< 0.1%
ValueCountFrequency (%)
1002963
23.2%
99.91
 
< 0.1%
99.891
 
< 0.1%
99.831
 
< 0.1%
99.81
 
< 0.1%

pct_f
Real number (ℝ≥0)

ZEROS

Distinct2428
Distinct (%)19.1%
Missing65
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean50.49293974
Minimum0
Maximum100
Zeros998
Zeros (%)7.8%
Memory size99.8 KiB
2021-03-18T16:43:51.167544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q126.19
median54.55
Q375
95-th percentile93.33
Maximum100
Range100
Interquartile range (IQR)48.81

Descriptive statistics

Standard deviation29.64501315
Coefficient of variation (CV)0.5871120457
Kurtosis-1.091517092
Mean50.49293974
Median Absolute Deviation (MAD)23.23
Skewness-0.2518570681
Sum641007.87
Variance878.8268044
MonotocityNot monotonic
2021-03-18T16:43:51.301456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0998
 
7.8%
50341
 
2.7%
100322
 
2.5%
66.67205
 
1.6%
60163
 
1.3%
33.33158
 
1.2%
75151
 
1.2%
80150
 
1.2%
25102
 
0.8%
4088
 
0.7%
Other values (2418)10017
78.5%
ValueCountFrequency (%)
0998
7.8%
0.931
 
< 0.1%
1.651
 
< 0.1%
1.771
 
< 0.1%
1.851
 
< 0.1%
ValueCountFrequency (%)
100322
2.5%
99.241
 
< 0.1%
991
 
< 0.1%
98.261
 
< 0.1%
98.111
 
< 0.1%

pct_aca_orig
Real number (ℝ≥0)

MISSING
ZEROS

Distinct1854
Distinct (%)14.7%
Missing159
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean72.64614554
Minimum0
Maximum100
Zeros204
Zeros (%)1.6%
Memory size99.8 KiB
2021-03-18T16:43:51.414635image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19.88
Q159.09
median80
Q392.86
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)33.77

Descriptive statistics

Standard deviation25.27910321
Coefficient of variation (CV)0.3479758357
Kurtosis0.2725950575
Mean72.64614554
Median Absolute Deviation (MAD)15.24
Skewness-1.02042246
Sum915414.08
Variance639.033059
MonotocityNot monotonic
2021-03-18T16:43:51.530491image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1001762
 
13.8%
50293
 
2.3%
66.67259
 
2.0%
75232
 
1.8%
80213
 
1.7%
0204
 
1.6%
83.33188
 
1.5%
85.71180
 
1.4%
60152
 
1.2%
90.91146
 
1.1%
Other values (1844)8972
70.3%
(Missing)159
 
1.2%
ValueCountFrequency (%)
0204
1.6%
2.71
 
< 0.1%
3.451
 
< 0.1%
3.951
 
< 0.1%
3.971
 
< 0.1%
ValueCountFrequency (%)
1001762
13.8%
99.371
 
< 0.1%
99.331
 
< 0.1%
99.191
 
< 0.1%
99.123
 
< 0.1%

pct_aca_orig_idf
Real number (ℝ≥0)

MISSING

Distinct1719
Distinct (%)13.6%
Missing159
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean78.34563447
Minimum0
Maximum100
Zeros110
Zeros (%)0.9%
Memory size99.8 KiB
2021-03-18T16:43:51.666667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33.33
Q167.05
median84.44
Q394.74
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)27.69

Descriptive statistics

Standard deviation21.38006653
Coefficient of variation (CV)0.2728941654
Kurtosis1.463751446
Mean78.34563447
Median Absolute Deviation (MAD)12.11
Skewness-1.302546521
Sum987233.34
Variance457.1072448
MonotocityNot monotonic
2021-03-18T16:43:51.786586image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1002147
 
16.8%
50272
 
2.1%
66.67255
 
2.0%
75238
 
1.9%
80231
 
1.8%
83.33210
 
1.6%
85.71203
 
1.6%
90.91167
 
1.3%
87.5158
 
1.2%
90156
 
1.2%
Other values (1709)8564
67.1%
(Missing)159
 
1.2%
ValueCountFrequency (%)
0110
0.9%
3.951
 
< 0.1%
41
 
< 0.1%
4.352
 
< 0.1%
4.552
 
< 0.1%
ValueCountFrequency (%)
1002147
16.8%
99.371
 
< 0.1%
99.331
 
< 0.1%
99.191
 
< 0.1%
99.131
 
< 0.1%

pct_etab_orig
Real number (ℝ≥0)

MISSING
ZEROS

Distinct551
Distinct (%)9.2%
Missing6748
Missing (%)52.9%
Infinite0
Infinite (%)0.0%
Mean22.42213573
Minimum0
Maximum100
Zeros954
Zeros (%)7.5%
Memory size99.8 KiB
2021-03-18T16:43:51.921080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.06
median16.67
Q333.33
95-th percentile64.71
Maximum100
Range100
Interquartile range (IQR)27.27

Descriptive statistics

Standard deviation20.76173216
Coefficient of variation (CV)0.9259480192
Kurtosis0.8362385694
Mean22.42213573
Median Absolute Deviation (MAD)12.67
Skewness1.107964021
Sum134801.88
Variance431.0495225
MonotocityNot monotonic
2021-03-18T16:43:52.023027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0954
 
7.5%
33.33132
 
1.0%
25125
 
1.0%
50121
 
0.9%
20115
 
0.9%
16.67105
 
0.8%
14.2995
 
0.7%
12.587
 
0.7%
11.1169
 
0.5%
1067
 
0.5%
Other values (541)4142
32.5%
(Missing)6748
52.9%
ValueCountFrequency (%)
0954
7.5%
0.732
 
< 0.1%
1.061
 
< 0.1%
1.114
 
< 0.1%
1.181
 
< 0.1%
ValueCountFrequency (%)
10017
0.1%
96.432
 
< 0.1%
961
 
< 0.1%
95.651
 
< 0.1%
95.241
 
< 0.1%

pct_bours
Real number (ℝ≥0)

MISSING
ZEROS

Distinct1606
Distinct (%)12.7%
Missing159
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean26.91673201
Minimum0
Maximum100
Zeros817
Zeros (%)6.4%
Memory size99.8 KiB
2021-03-18T16:43:52.126044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114.29
median25
Q336.84
95-th percentile60
Maximum100
Range100
Interquartile range (IQR)22.55

Descriptive statistics

Standard deviation17.84088773
Coefficient of variation (CV)0.6628177496
Kurtosis0.885196047
Mean26.91673201
Median Absolute Deviation (MAD)11.36
Skewness0.8319858144
Sum339177.74
Variance318.2972751
MonotocityNot monotonic
2021-03-18T16:43:52.234211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0817
 
6.4%
33.33441
 
3.5%
25381
 
3.0%
50319
 
2.5%
20307
 
2.4%
16.67264
 
2.1%
14.29223
 
1.7%
28.57202
 
1.6%
40190
 
1.5%
22.22156
 
1.2%
Other values (1596)9301
72.9%
(Missing)159
 
1.2%
ValueCountFrequency (%)
0817
6.4%
0.911
 
< 0.1%
0.951
 
< 0.1%
0.982
 
< 0.1%
11
 
< 0.1%
ValueCountFrequency (%)
10049
0.4%
94.121
 
< 0.1%
92.861
 
< 0.1%
92.311
 
< 0.1%
91.672
 
< 0.1%

pct_neobac
Real number (ℝ≥0)

Distinct2142
Distinct (%)16.9%
Missing65
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean76.21960457
Minimum0
Maximum100
Zeros94
Zeros (%)0.7%
Memory size99.8 KiB
2021-03-18T16:43:52.343683image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile36
Q166.67
median80
Q391.43
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)24.76

Descriptive statistics

Standard deviation20.06611108
Coefficient of variation (CV)0.2632670584
Kurtosis1.581190371
Mean76.21960457
Median Absolute Deviation (MAD)12.31
Skewness-1.209422211
Sum967607.88
Variance402.648814
MonotocityNot monotonic
2021-03-18T16:43:52.449488image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1001338
 
10.5%
80257
 
2.0%
66.67242
 
1.9%
75239
 
1.9%
83.33202
 
1.6%
85.71178
 
1.4%
50158
 
1.2%
87.5156
 
1.2%
91.67138
 
1.1%
90135
 
1.1%
Other values (2132)9652
75.6%
ValueCountFrequency (%)
094
0.7%
1.491
 
< 0.1%
2.221
 
< 0.1%
2.781
 
< 0.1%
3.331
 
< 0.1%
ValueCountFrequency (%)
1001338
10.5%
99.861
 
< 0.1%
99.781
 
< 0.1%
99.61
 
< 0.1%
99.541
 
< 0.1%

pct_mention_nonrenseignee
Real number (ℝ≥0)

MISSING
ZEROS

Distinct457
Distinct (%)3.6%
Missing159
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean0.4452298478
Minimum0
Maximum100
Zeros11216
Zeros (%)87.9%
Memory size99.8 KiB
2021-03-18T16:43:52.597806image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2.777777778
Maximum100
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.395115807
Coefficient of variation (CV)5.379504136
Kurtosis588.6167595
Mean0.4452298478
Median Absolute Deviation (MAD)0
Skewness18.39012061
Sum5610.341312
Variance5.73657973
MonotocityNot monotonic
2021-03-18T16:43:52.740292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
011216
87.9%
5.55555555631
 
0.2%
6.2528
 
0.2%
4.76190476228
 
0.2%
3.84615384620
 
0.2%
3.70370370420
 
0.2%
7.14285714319
 
0.1%
3.33333333319
 
0.1%
519
 
0.1%
419
 
0.1%
Other values (447)1182
 
9.3%
(Missing)159
 
1.2%
ValueCountFrequency (%)
011216
87.9%
0.12610340481
 
< 0.1%
0.13315579231
 
< 0.1%
0.15060240961
 
< 0.1%
0.16501650161
 
< 0.1%
ValueCountFrequency (%)
1002
< 0.1%
66.666666671
 
< 0.1%
57.142857141
 
< 0.1%
501
 
< 0.1%
33.333333334
< 0.1%

pct_sansmention
Real number (ℝ≥0)

MISSING
ZEROS

Distinct1730
Distinct (%)13.7%
Missing159
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean29.84594597
Minimum0
Maximum100
Zeros1924
Zeros (%)15.1%
Memory size99.8 KiB
2021-03-18T16:43:52.899231image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19.677419355
median28.57142857
Q345.96774194
95-th percentile70
Maximum100
Range100
Interquartile range (IQR)36.29032258

Descriptive statistics

Standard deviation23.13384884
Coefficient of variation (CV)0.7751085814
Kurtosis-0.2159264323
Mean29.84594597
Median Absolute Deviation (MAD)18.0952381
Skewness0.5434777234
Sum376088.7652
Variance535.1749621
MonotocityNot monotonic
2021-03-18T16:43:53.045178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01924
 
15.1%
50458
 
3.6%
33.33333333346
 
2.7%
25256
 
2.0%
40185
 
1.4%
20173
 
1.4%
66.66666667156
 
1.2%
28.57142857142
 
1.1%
100141
 
1.1%
42.85714286136
 
1.1%
Other values (1720)8684
68.1%
(Missing)159
 
1.2%
ValueCountFrequency (%)
01924
15.1%
0.19417475731
 
< 0.1%
0.21
 
< 0.1%
0.49019607841
 
< 0.1%
0.51612903231
 
< 0.1%
ValueCountFrequency (%)
100141
1.1%
95.652173911
 
< 0.1%
95.238095241
 
< 0.1%
94.117647061
 
< 0.1%
93.333333331
 
< 0.1%

pct_ab
Real number (ℝ≥0)

MISSING
ZEROS

Distinct1680
Distinct (%)13.3%
Missing159
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean34.24240601
Minimum0
Maximum100
Zeros864
Zeros (%)6.8%
Memory size99.8 KiB
2021-03-18T16:43:53.192113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q124.24242424
median35.29411765
Q345.45454545
95-th percentile61.11111111
Maximum100
Range100
Interquartile range (IQR)21.21212121

Descriptive statistics

Standard deviation17.50637086
Coefficient of variation (CV)0.5112482709
Kurtosis0.4282666275
Mean34.24240601
Median Absolute Deviation (MAD)10.35805627
Skewness0.01839356449
Sum431488.5581
Variance306.4730207
MonotocityNot monotonic
2021-03-18T16:43:53.339691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0864
 
6.8%
50638
 
5.0%
33.33333333535
 
4.2%
40333
 
2.6%
25307
 
2.4%
42.85714286226
 
1.8%
28.57142857201
 
1.6%
20199
 
1.6%
37.5192
 
1.5%
36.36363636163
 
1.3%
Other values (1670)8943
70.1%
(Missing)159
 
1.2%
ValueCountFrequency (%)
0864
6.8%
0.18518518521
 
< 0.1%
0.45045045051
 
< 0.1%
0.52631578951
 
< 0.1%
0.69444444441
 
< 0.1%
ValueCountFrequency (%)
10060
0.5%
94.736842111
 
< 0.1%
94.117647061
 
< 0.1%
92.307692311
 
< 0.1%
90.909090911
 
< 0.1%

pct_b
Real number (ℝ≥0)

MISSING
ZEROS

Distinct1710
Distinct (%)13.6%
Missing159
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean23.65366738
Minimum0
Maximum100
Zeros1260
Zeros (%)9.9%
Memory size99.8 KiB
2021-03-18T16:43:53.481292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111.11111111
median21.05263158
Q333.33333333
95-th percentile54.28571429
Maximum100
Range100
Interquartile range (IQR)22.22222222

Descriptive statistics

Standard deviation16.93764422
Coefficient of variation (CV)0.7160684199
Kurtosis1.062810006
Mean23.65366738
Median Absolute Deviation (MAD)11.05263158
Skewness0.8642991343
Sum298059.8626
Variance286.8837919
MonotocityNot monotonic
2021-03-18T16:43:53.616807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01260
 
9.9%
25349
 
2.7%
33.33333333340
 
2.7%
20318
 
2.5%
16.66666667291
 
2.3%
50270
 
2.1%
14.28571429234
 
1.8%
12.5204
 
1.6%
28.57142857189
 
1.5%
11.11111111183
 
1.4%
Other values (1700)8963
70.2%
ValueCountFrequency (%)
01260
9.9%
0.33444816051
 
< 0.1%
0.70921985821
 
< 0.1%
1.0948905111
 
< 0.1%
1.1173184361
 
< 0.1%
ValueCountFrequency (%)
10044
0.3%
92.857142861
 
< 0.1%
901
 
< 0.1%
87.53
 
< 0.1%
85.714285712
 
< 0.1%

pct_tb
Real number (ℝ≥0)

MISSING
ZEROS

Distinct1593
Distinct (%)12.6%
Missing159
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean11.81273756
Minimum0
Maximum100
Zeros4549
Zeros (%)35.7%
Memory size99.8 KiB
2021-03-18T16:43:53.761607image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.761904762
Q312.72727273
95-th percentile56.41025641
Maximum100
Range100
Interquartile range (IQR)12.72727273

Descriptive statistics

Standard deviation19.39267473
Coefficient of variation (CV)1.641674898
Kurtosis7.062392579
Mean11.81273756
Median Absolute Deviation (MAD)4.761904762
Skewness2.620825351
Sum148852.306
Variance376.0758331
MonotocityNot monotonic
2021-03-18T16:43:53.908170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04549
35.7%
10168
 
1.3%
9.090909091161
 
1.3%
14.28571429156
 
1.2%
7.692307692150
 
1.2%
8.333333333149
 
1.2%
12.5148
 
1.2%
11.11111111144
 
1.1%
7.142857143138
 
1.1%
20135
 
1.1%
Other values (1583)6703
52.5%
(Missing)159
 
1.2%
ValueCountFrequency (%)
04549
35.7%
0.38167938931
 
< 0.1%
0.43478260871
 
< 0.1%
0.44444444441
 
< 0.1%
0.50761421321
 
< 0.1%
ValueCountFrequency (%)
10080
0.6%
99.665551841
 
< 0.1%
99.290780141
 
< 0.1%
99.285714291
 
< 0.1%
98.795180722
 
< 0.1%

pct_bg
Real number (ℝ≥0)

MISSING
ZEROS

Distinct1746
Distinct (%)13.9%
Missing159
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean48.60558926
Minimum0
Maximum100
Zeros1514
Zeros (%)11.9%
Memory size99.8 KiB
2021-03-18T16:43:54.034568image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113.04347826
median45.45454545
Q385.18518519
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)72.14170692

Descriptive statistics

Standard deviation36.63540864
Coefficient of variation (CV)0.7537283098
Kurtosis-1.517988756
Mean48.60558926
Median Absolute Deviation (MAD)35.42780749
Skewness0.1101860963
Sum612479.0303
Variance1342.153166
MonotocityNot monotonic
2021-03-18T16:43:54.152775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1001910
 
15.0%
01514
 
11.9%
50227
 
1.8%
25173
 
1.4%
33.33333333161
 
1.3%
20154
 
1.2%
16.66666667148
 
1.2%
14.28571429134
 
1.1%
66.66666667119
 
0.9%
9.090909091107
 
0.8%
Other values (1736)7954
62.3%
(Missing)159
 
1.2%
ValueCountFrequency (%)
01514
11.9%
0.87719298251
 
< 0.1%
1.8867924531
 
< 0.1%
2.0833333331
 
< 0.1%
2.1276595741
 
< 0.1%
ValueCountFrequency (%)
1001910
15.0%
99.840764331
 
< 0.1%
99.816513761
 
< 0.1%
99.812030081
 
< 0.1%
99.780219781
 
< 0.1%

pct_bg_mention
Real number (ℝ≥0)

MISSING
ZEROS

Distinct1863
Distinct (%)14.8%
Missing159
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean34.33266646
Minimum0
Maximum100
Zeros3025
Zeros (%)23.7%
Memory size99.8 KiB
2021-03-18T16:43:54.284399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.33
median23.73
Q358.33
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)55

Descriptive statistics

Standard deviation34.1950444
Coefficient of variation (CV)0.9959915129
Kurtosis-0.8624627661
Mean34.33266646
Median Absolute Deviation (MAD)23.73
Skewness0.700887524
Sum432625.93
Variance1169.301062
MonotocityNot monotonic
2021-03-18T16:43:54.387661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03025
23.7%
1001000
 
7.8%
50203
 
1.6%
33.33177
 
1.4%
25159
 
1.2%
14.29126
 
1.0%
20112
 
0.9%
8.33109
 
0.9%
9.09107
 
0.8%
11.11104
 
0.8%
Other values (1853)7479
58.6%
(Missing)159
 
1.2%
ValueCountFrequency (%)
03025
23.7%
0.881
 
< 0.1%
1.411
 
< 0.1%
1.561
 
< 0.1%
1.631
 
< 0.1%
ValueCountFrequency (%)
1001000
7.8%
99.731
 
< 0.1%
99.641
 
< 0.1%
99.611
 
< 0.1%
99.541
 
< 0.1%

pct_bt
Real number (ℝ≥0)

MISSING
ZEROS

Distinct1621
Distinct (%)12.9%
Missing159
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean27.92127258
Minimum0
Maximum100
Zeros2442
Zeros (%)19.1%
Memory size99.8 KiB
2021-03-18T16:43:54.495645image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17.317073171
median26.31578947
Q343.47826087
95-th percentile66.66666667
Maximum100
Range100
Interquartile range (IQR)36.1611877

Descriptive statistics

Standard deviation23.05687801
Coefficient of variation (CV)0.8257817744
Kurtosis0.2399736746
Mean27.92127258
Median Absolute Deviation (MAD)17.98245614
Skewness0.6943960338
Sum351835.9558
Variance531.6196237
MonotocityNot monotonic
2021-03-18T16:43:54.604629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02442
 
19.1%
50450
 
3.5%
33.33333333331
 
2.6%
25244
 
1.9%
100227
 
1.8%
40211
 
1.7%
20170
 
1.3%
16.66666667148
 
1.2%
42.85714286140
 
1.1%
28.57142857136
 
1.1%
Other values (1611)8102
63.5%
(Missing)159
 
1.2%
ValueCountFrequency (%)
02442
19.1%
0.15923566881
 
< 0.1%
0.18348623851
 
< 0.1%
0.18796992481
 
< 0.1%
0.19342359771
 
< 0.1%
ValueCountFrequency (%)
100227
1.8%
93.751
 
< 0.1%
92.857142861
 
< 0.1%
92.307692314
 
< 0.1%
921
 
< 0.1%

pct_bt_mention
Real number (ℝ≥0)

MISSING
ZEROS

Distinct1362
Distinct (%)10.8%
Missing159
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean17.67730021
Minimum0
Maximum100
Zeros3410
Zeros (%)26.7%
Memory size99.8 KiB
2021-03-18T16:43:54.710318image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median12
Q328.57
95-th percentile50.94
Maximum100
Range100
Interquartile range (IQR)28.57

Descriptive statistics

Standard deviation19.46508372
Coefficient of variation (CV)1.10113442
Kurtosis3.223308324
Mean17.67730021
Median Absolute Deviation (MAD)12
Skewness1.568671515
Sum222751.66
Variance378.8894841
MonotocityNot monotonic
2021-03-18T16:43:54.845478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03410
26.7%
25252
 
2.0%
33.33246
 
1.9%
20233
 
1.8%
14.29183
 
1.4%
16.67165
 
1.3%
12.5147
 
1.2%
50146
 
1.1%
10142
 
1.1%
100137
 
1.1%
Other values (1352)7540
59.1%
(Missing)159
 
1.2%
ValueCountFrequency (%)
03410
26.7%
0.161
 
< 0.1%
0.181
 
< 0.1%
0.192
 
< 0.1%
0.221
 
< 0.1%
ValueCountFrequency (%)
100137
1.1%
97.831
 
< 0.1%
97.561
 
< 0.1%
97.52
 
< 0.1%
97.062
 
< 0.1%

pct_bp
Real number (ℝ≥0)

MISSING
ZEROS

Distinct1212
Distinct (%)9.6%
Missing159
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean23.47313816
Minimum0
Maximum100
Zeros4157
Zeros (%)32.6%
Memory size99.8 KiB
2021-03-18T16:43:54.993975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median12.5
Q341.66666667
95-th percentile76.92307692
Maximum100
Range100
Interquartile range (IQR)41.66666667

Descriptive statistics

Standard deviation26.68194262
Coefficient of variation (CV)1.136701128
Kurtosis0.1842604332
Mean23.47313816
Median Absolute Deviation (MAD)12.5
Skewness1.030003853
Sum295785.014
Variance711.9260618
MonotocityNot monotonic
2021-03-18T16:43:55.120873image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04157
32.6%
50345
 
2.7%
100291
 
2.3%
33.33333333222
 
1.7%
25155
 
1.2%
40135
 
1.1%
20134
 
1.1%
66.66666667119
 
0.9%
16.66666667106
 
0.8%
60104
 
0.8%
Other values (1202)6833
53.6%
(Missing)159
 
1.2%
ValueCountFrequency (%)
04157
32.6%
0.12903225811
 
< 0.1%
0.14471780031
 
< 0.1%
0.17035775131
 
< 0.1%
0.19342359771
 
< 0.1%
ValueCountFrequency (%)
100291
2.3%
95.652173913
 
< 0.1%
95.238095241
 
< 0.1%
94.736842112
 
< 0.1%
94.444444442
 
< 0.1%

pct_bp_mention
Real number (ℝ≥0)

MISSING
ZEROS

Distinct1001
Distinct (%)7.9%
Missing159
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean17.6987382
Minimum0
Maximum100
Zeros4602
Zeros (%)36.1%
Memory size99.8 KiB
2021-03-18T16:43:55.273234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8
Q331.58
95-th percentile60
Maximum100
Range100
Interquartile range (IQR)31.58

Descriptive statistics

Standard deviation21.30921498
Coefficient of variation (CV)1.203996282
Kurtosis0.916380306
Mean17.6987382
Median Absolute Deviation (MAD)8
Skewness1.207752526
Sum223021.8
Variance454.0826429
MonotocityNot monotonic
2021-03-18T16:43:55.407305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04602
36.1%
33.33279
 
2.2%
50256
 
2.0%
25196
 
1.5%
20182
 
1.4%
28.57123
 
1.0%
40121
 
0.9%
12.5120
 
0.9%
16.67118
 
0.9%
14.29110
 
0.9%
Other values (991)6494
50.9%
(Missing)159
 
1.2%
ValueCountFrequency (%)
04602
36.1%
0.131
 
< 0.1%
0.141
 
< 0.1%
0.161
 
< 0.1%
0.171
 
< 0.1%
ValueCountFrequency (%)
10063
0.5%
94.741
 
< 0.1%
94.441
 
< 0.1%
92.591
 
< 0.1%
92.313
 
< 0.1%

prop_tot_bg
Real number (ℝ≥0)

ZEROS

Distinct1144
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean154.1234326
Minimum0
Maximum4882
Zeros741
Zeros (%)5.8%
Memory size99.8 KiB
2021-03-18T16:43:55.535788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median33
Q3165
95-th percentile667
Maximum4882
Range4882
Interquartile range (IQR)155

Descriptive statistics

Standard deviation318.8101216
Coefficient of variation (CV)2.06853764
Kurtosis31.65975003
Mean154.1234326
Median Absolute Deviation (MAD)30
Skewness4.744369237
Sum1966615
Variance101639.8936
MonotocityNot monotonic
2021-03-18T16:43:55.662237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0741
 
5.8%
1292
 
2.3%
8283
 
2.2%
2268
 
2.1%
6264
 
2.1%
3262
 
2.1%
7256
 
2.0%
5247
 
1.9%
10240
 
1.9%
9235
 
1.8%
Other values (1134)9672
75.8%
ValueCountFrequency (%)
0741
5.8%
1292
 
2.3%
2268
 
2.1%
3262
 
2.1%
4229
 
1.8%
ValueCountFrequency (%)
48821
< 0.1%
40661
< 0.1%
38461
< 0.1%
35831
< 0.1%
35721
< 0.1%

prop_tot_bg_brs
Real number (ℝ≥0)

ZEROS

Distinct350
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.05658307
Minimum0
Maximum943
Zeros1631
Zeros (%)12.8%
Memory size99.8 KiB
2021-03-18T16:43:55.781002image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median7
Q326
95-th percentile117
Maximum943
Range943
Interquartile range (IQR)24

Descriptive statistics

Standard deviation53.9347569
Coefficient of variation (CV)2.069909042
Kurtosis43.11271905
Mean26.05658307
Median Absolute Deviation (MAD)7
Skewness5.328044594
Sum332482
Variance2908.958002
MonotocityNot monotonic
2021-03-18T16:43:55.881634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01631
 
12.8%
11040
 
8.2%
2964
 
7.6%
3848
 
6.6%
4664
 
5.2%
5532
 
4.2%
6461
 
3.6%
7373
 
2.9%
8303
 
2.4%
9297
 
2.3%
Other values (340)5647
44.3%
ValueCountFrequency (%)
01631
12.8%
11040
8.2%
2964
7.6%
3848
6.6%
4664
5.2%
ValueCountFrequency (%)
9431
< 0.1%
7521
< 0.1%
7031
< 0.1%
6981
< 0.1%
6901
< 0.1%

prop_tot_bt
Real number (ℝ≥0)

ZEROS

Distinct326
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.06332288
Minimum0
Maximum1178
Zeros1768
Zeros (%)13.9%
Memory size99.8 KiB
2021-03-18T16:43:56.649168image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median23
Q348
95-th percentile127
Maximum1178
Range1178
Interquartile range (IQR)43

Descriptive statistics

Standard deviation50.48663014
Coefficient of variation (CV)1.362172256
Kurtosis44.38301925
Mean37.06332288
Median Absolute Deviation (MAD)20
Skewness4.418220732
Sum472928
Variance2548.899822
MonotocityNot monotonic
2021-03-18T16:43:56.772453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01768
 
13.9%
1381
 
3.0%
2293
 
2.3%
3277
 
2.2%
4263
 
2.1%
7220
 
1.7%
5212
 
1.7%
9200
 
1.6%
16190
 
1.5%
6190
 
1.5%
Other values (316)8766
68.7%
ValueCountFrequency (%)
01768
13.9%
1381
 
3.0%
2293
 
2.3%
3277
 
2.2%
4263
 
2.1%
ValueCountFrequency (%)
11781
< 0.1%
8961
< 0.1%
7421
< 0.1%
6281
< 0.1%
5521
< 0.1%

prop_tot_bt_brs
Real number (ℝ≥0)

ZEROS

Distinct144
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.00274295
Minimum0
Maximum454
Zeros2514
Zeros (%)19.7%
Memory size99.8 KiB
2021-03-18T16:43:56.878744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q314
95-th percentile38
Maximum454
Range454
Interquartile range (IQR)13

Descriptive statistics

Standard deviation17.14742214
Coefficient of variation (CV)1.558467941
Kurtosis92.41281513
Mean11.00274295
Median Absolute Deviation (MAD)6
Skewness6.524453883
Sum140395
Variance294.0340861
MonotocityNot monotonic
2021-03-18T16:43:56.987641image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02514
19.7%
1857
 
6.7%
2766
 
6.0%
3705
 
5.5%
4627
 
4.9%
5616
 
4.8%
6575
 
4.5%
7521
 
4.1%
9444
 
3.5%
8420
 
3.3%
Other values (134)4715
37.0%
ValueCountFrequency (%)
02514
19.7%
1857
 
6.7%
2766
 
6.0%
3705
 
5.5%
4627
 
4.9%
ValueCountFrequency (%)
4541
< 0.1%
3681
< 0.1%
3201
< 0.1%
3061
< 0.1%
2631
< 0.1%

prop_tot_bp
Real number (ℝ≥0)

ZEROS

Distinct171
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.40877743
Minimum0
Maximum829
Zeros3269
Zeros (%)25.6%
Memory size99.8 KiB
2021-03-18T16:43:57.092261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median10
Q329
95-th percentile60
Maximum829
Range829
Interquartile range (IQR)29

Descriptive statistics

Standard deviation26.00371629
Coefficient of variation (CV)1.412571606
Kurtosis125.4884109
Mean18.40877743
Median Absolute Deviation (MAD)10
Skewness6.4253482
Sum234896
Variance676.193261
MonotocityNot monotonic
2021-03-18T16:43:57.192583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03269
25.6%
1720
 
5.6%
2473
 
3.7%
3412
 
3.2%
4290
 
2.3%
6275
 
2.2%
5246
 
1.9%
7212
 
1.7%
8211
 
1.7%
19209
 
1.6%
Other values (161)6443
50.5%
ValueCountFrequency (%)
03269
25.6%
1720
 
5.6%
2473
 
3.7%
3412
 
3.2%
4290
 
2.3%
ValueCountFrequency (%)
8291
< 0.1%
5921
< 0.1%
5781
< 0.1%
4281
< 0.1%
3751
< 0.1%

prop_tot_bp_brs
Real number (ℝ≥0)

ZEROS

Distinct101
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.386990596
Minimum0
Maximum393
Zeros4017
Zeros (%)31.5%
Memory size99.8 KiB
2021-03-18T16:43:57.316856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q310
95-th percentile27
Maximum393
Range393
Interquartile range (IQR)10

Descriptive statistics

Standard deviation12.10694125
Coefficient of variation (CV)1.63895447
Kurtosis154.5453307
Mean7.386990596
Median Absolute Deviation (MAD)3
Skewness7.926662574
Sum94258
Variance146.5780265
MonotocityNot monotonic
2021-03-18T16:43:57.470066image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04017
31.5%
11079
 
8.5%
2704
 
5.5%
3610
 
4.8%
4581
 
4.6%
5520
 
4.1%
6517
 
4.1%
7444
 
3.5%
8382
 
3.0%
9376
 
2.9%
Other values (91)3530
27.7%
ValueCountFrequency (%)
04017
31.5%
11079
 
8.5%
2704
 
5.5%
3610
 
4.8%
4581
 
4.6%
ValueCountFrequency (%)
3931
< 0.1%
2881
< 0.1%
2611
< 0.1%
2201
< 0.1%
2071
< 0.1%

prop_tot_at
Real number (ℝ≥0)

ZEROS

Distinct585
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.82194357
Minimum0
Maximum1928
Zeros467
Zeros (%)3.7%
Memory size99.8 KiB
2021-03-18T16:43:57.654004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q18
median21
Q355
95-th percentile253
Maximum1928
Range1928
Interquartile range (IQR)47

Descriptive statistics

Standard deviation108.5813014
Coefficient of variation (CV)1.877856306
Kurtosis39.37448297
Mean57.82194357
Median Absolute Deviation (MAD)16
Skewness4.871950859
Sum737808
Variance11789.89901
MonotocityNot monotonic
2021-03-18T16:43:57.789320image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0467
 
3.7%
6376
 
2.9%
2370
 
2.9%
5364
 
2.9%
3364
 
2.9%
4351
 
2.8%
8333
 
2.6%
7330
 
2.6%
1330
 
2.6%
9308
 
2.4%
Other values (575)9167
71.8%
ValueCountFrequency (%)
0467
3.7%
1330
2.6%
2370
2.9%
3364
2.9%
4351
2.8%
ValueCountFrequency (%)
19281
< 0.1%
18501
< 0.1%
18241
< 0.1%
15211
< 0.1%
12662
< 0.1%

lib_grp1
Categorical

MISSING

Distinct28
Distinct (%)0.2%
Missing234
Missing (%)1.8%
Memory size99.8 KiB
Tous les candidats
6500 
Bacheliers professionnels toutes séries
1889 
Bacheliers technologiques toutes séries
1638 
Tous les candidats sauf les Bac technologiques et Bac professionnels
1274 
Tous les candidats sauf les Bac professionnels
670 
Other values (23)
 
555

Length

Max length68
Median length18
Mean length31.54143382
Min length5

Characters and Unicode

Total characters395088
Distinct characters41
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.1%

Sample

1st rowTous les candidats
2nd rowTous les candidats
3rd rowTous les candidats
4th rowTous les candidats
5th rowTous les candidats
ValueCountFrequency (%)
Tous les candidats6500
50.9%
Bacheliers professionnels toutes séries1889
 
14.8%
Bacheliers technologiques toutes séries1638
 
12.8%
Tous les candidats sauf les Bac technologiques et Bac professionnels1274
 
10.0%
Tous les candidats sauf les Bac professionnels670
 
5.3%
Tous les candidats sauf les Bac technologiques465
 
3.6%
Autres candidats42
 
0.3%
Groupe par défaut6
 
< 0.1%
Parcours particuliers5
 
< 0.1%
Série S4
 
< 0.1%
Other values (18)33
 
0.3%
(Missing)234
 
1.8%
2021-03-18T16:43:58.069042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
les11330
20.8%
candidats8961
16.4%
tous8917
16.4%
professionnels3834
 
7.0%
bac3697
 
6.8%
toutes3530
 
6.5%
séries3527
 
6.5%
bacheliers3527
 
6.5%
technologiques3377
 
6.2%
sauf2410
 
4.4%
Other values (38)1427
 
2.6%

Most occurring characters

ValueCountFrequency (%)
s60701
15.4%
42011
10.6%
e41227
10.4%
a27610
 
7.0%
o26894
 
6.8%
i23270
 
5.9%
l22096
 
5.6%
t20742
 
5.2%
n20032
 
5.1%
c19583
 
5.0%
Other values (31)90922
23.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter336758
85.2%
Space Separator42011
 
10.6%
Uppercase Letter16297
 
4.1%
Other Punctuation16
 
< 0.1%
Dash Punctuation5
 
< 0.1%
Decimal Number1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
s60701
18.0%
e41227
12.2%
a27610
8.2%
o26894
8.0%
i23270
 
6.9%
l22096
 
6.6%
t20742
 
6.2%
n20032
 
5.9%
c19583
 
5.8%
u18311
 
5.4%
Other values (12)56292
16.7%
ValueCountFrequency (%)
T8935
54.8%
B7222
44.3%
A53
 
0.3%
S32
 
0.2%
P11
 
0.1%
E8
 
< 0.1%
I8
 
< 0.1%
C7
 
< 0.1%
G6
 
< 0.1%
V5
 
< 0.1%
Other values (4)10
 
0.1%
ValueCountFrequency (%)
/13
81.2%
'3
 
18.8%
ValueCountFrequency (%)
42011
100.0%
ValueCountFrequency (%)
21
100.0%
ValueCountFrequency (%)
-5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin353055
89.4%
Common42033
 
10.6%

Most frequent character per script

ValueCountFrequency (%)
s60701
17.2%
e41227
11.7%
a27610
 
7.8%
o26894
 
7.6%
i23270
 
6.6%
l22096
 
6.3%
t20742
 
5.9%
n20032
 
5.7%
c19583
 
5.5%
u18311
 
5.2%
Other values (26)72589
20.6%
ValueCountFrequency (%)
42011
99.9%
/13
 
< 0.1%
-5
 
< 0.1%
'3
 
< 0.1%
21
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII391524
99.1%
None3564
 
0.9%

Most frequent character per block

ValueCountFrequency (%)
s60701
15.5%
42011
10.7%
e41227
10.5%
a27610
 
7.1%
o26894
 
6.9%
i23270
 
5.9%
l22096
 
5.6%
t20742
 
5.3%
n20032
 
5.1%
c19583
 
5.0%
Other values (29)87358
22.3%
ValueCountFrequency (%)
é3556
99.8%
ç8
 
0.2%

ran_grp1
Real number (ℝ≥0)

MISSING

Distinct1911
Distinct (%)15.3%
Missing234
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean395.5339294
Minimum1
Maximum13932
Zeros0
Zeros (%)0.0%
Memory size99.8 KiB
2021-03-18T16:43:58.203657image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q137
median90
Q3336
95-th percentile1852.5
Maximum13932
Range13931
Interquartile range (IQR)299

Descriptive statistics

Standard deviation911.7416397
Coefficient of variation (CV)2.305090845
Kurtosis64.54541677
Mean395.5339294
Median Absolute Deviation (MAD)70
Skewness6.491024722
Sum4954458
Variance831272.8175
MonotocityNot monotonic
2021-03-18T16:43:58.345277image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16115
 
0.9%
24114
 
0.9%
30108
 
0.8%
23104
 
0.8%
21103
 
0.8%
10102
 
0.8%
29101
 
0.8%
1899
 
0.8%
1799
 
0.8%
2099
 
0.8%
Other values (1901)11482
90.0%
(Missing)234
 
1.8%
ValueCountFrequency (%)
141
0.3%
249
0.4%
361
0.5%
462
0.5%
572
0.6%
ValueCountFrequency (%)
139321
 
< 0.1%
139201
 
< 0.1%
139191
 
< 0.1%
139184
< 0.1%
128041
 
< 0.1%

lib_grp2
Categorical

MISSING

Distinct18
Distinct (%)0.3%
Missing6795
Missing (%)53.3%
Memory size99.8 KiB
Bacheliers professionnels toutes séries
1941 
Bacheliers technologiques toutes séries
1783 
Tous les candidats sauf les Bac technologiques et Bac professionnels
1246 
Tous les candidats sauf les Bac professionnels
620 
Tous les candidats sauf les Bac technologiques
339 
Other values (13)
 
36

Length

Max length68
Median length39
Mean length46.06588433
Min length5

Characters and Unicode

Total characters274783
Distinct characters38
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowTous les candidats sauf les Bac technologiques et Bac professionnels
2nd rowTous les candidats sauf les Bac technologiques et Bac professionnels
3rd rowTous les candidats sauf les Bac technologiques et Bac professionnels
4th rowTous les candidats sauf les Bac technologiques et Bac professionnels
5th rowBacheliers technologiques toutes séries
ValueCountFrequency (%)
Bacheliers professionnels toutes séries1941
 
15.2%
Bacheliers technologiques toutes séries1783
 
14.0%
Tous les candidats sauf les Bac technologiques et Bac professionnels1246
 
9.8%
Tous les candidats sauf les Bac professionnels620
 
4.9%
Tous les candidats sauf les Bac technologiques339
 
2.7%
Autres formations4
 
< 0.1%
Bac S Sciences de l'Ingénieur4
 
< 0.1%
Parcours particuliers4
 
< 0.1%
Toutes les filles4
 
< 0.1%
Série S3
 
< 0.1%
Other values (8)17
 
0.1%
(Missing)6795
53.3%
2021-03-18T16:43:58.631200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
les4417
12.9%
professionnels3807
11.1%
toutes3728
10.9%
séries3724
10.9%
bacheliers3724
10.9%
bac3463
10.1%
technologiques3368
9.9%
candidats2210
6.5%
tous2208
6.5%
sauf2205
6.5%
Other values (23)1323
 
3.9%

Most occurring characters

ValueCountFrequency (%)
s40773
14.8%
e34954
12.7%
28212
10.3%
o20301
 
7.4%
i16870
 
6.1%
l15340
 
5.6%
t14296
 
5.2%
a13839
 
5.0%
n13216
 
4.8%
c12784
 
4.7%
Other values (28)64198
23.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter237062
86.3%
Space Separator28212
 
10.3%
Uppercase Letter9491
 
3.5%
Other Punctuation13
 
< 0.1%
Decimal Number3
 
< 0.1%
Dash Punctuation2
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
s40773
17.2%
e34954
14.7%
o20301
8.6%
i16870
 
7.1%
l15340
 
6.5%
t14296
 
6.0%
a13839
 
5.8%
n13216
 
5.6%
c12784
 
5.4%
u11527
 
4.9%
Other values (11)43162
18.2%
ValueCountFrequency (%)
B7187
75.7%
T2224
 
23.4%
S31
 
0.3%
I11
 
0.1%
A9
 
0.1%
P6
 
0.1%
E5
 
0.1%
C5
 
0.1%
V4
 
< 0.1%
N4
 
< 0.1%
Other values (2)5
 
0.1%
ValueCountFrequency (%)
/9
69.2%
'4
30.8%
ValueCountFrequency (%)
28212
100.0%
ValueCountFrequency (%)
23
100.0%
ValueCountFrequency (%)
-2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin246553
89.7%
Common28230
 
10.3%

Most frequent character per script

ValueCountFrequency (%)
s40773
16.5%
e34954
14.2%
o20301
 
8.2%
i16870
 
6.8%
l15340
 
6.2%
t14296
 
5.8%
a13839
 
5.6%
n13216
 
5.4%
c12784
 
5.2%
u11527
 
4.7%
Other values (23)52653
21.4%
ValueCountFrequency (%)
28212
99.9%
/9
 
< 0.1%
'4
 
< 0.1%
23
 
< 0.1%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII271033
98.6%
None3750
 
1.4%

Most frequent character per block

ValueCountFrequency (%)
s40773
15.0%
e34954
12.9%
28212
10.4%
o20301
 
7.5%
i16870
 
6.2%
l15340
 
5.7%
t14296
 
5.3%
a13839
 
5.1%
n13216
 
4.9%
c12784
 
4.7%
Other values (26)60448
22.3%
ValueCountFrequency (%)
é3745
99.9%
ç5
 
0.1%

ran_grp2
Real number (ℝ≥0)

MISSING
SKEWED

Distinct468
Distinct (%)7.8%
Missing6795
Missing (%)53.3%
Infinite0
Infinite (%)0.0%
Mean81.05297569
Minimum1
Maximum8430
Zeros0
Zeros (%)0.0%
Memory size99.8 KiB
2021-03-18T16:43:58.763902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q123
median46
Q384
95-th percentile250.8
Maximum8430
Range8429
Interquartile range (IQR)61

Descriptive statistics

Standard deviation196.9885346
Coefficient of variation (CV)2.430367706
Kurtosis1019.877763
Mean81.05297569
Median Absolute Deviation (MAD)28
Skewness26.16505334
Sum483481
Variance38804.48277
MonotocityNot monotonic
2021-03-18T16:43:58.899564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1587
 
0.7%
2083
 
0.7%
1482
 
0.6%
4081
 
0.6%
1880
 
0.6%
3080
 
0.6%
2980
 
0.6%
1278
 
0.6%
3175
 
0.6%
975
 
0.6%
Other values (458)5164
40.5%
(Missing)6795
53.3%
ValueCountFrequency (%)
151
0.4%
251
0.4%
362
0.5%
451
0.4%
555
0.4%
ValueCountFrequency (%)
84301
< 0.1%
81081
< 0.1%
29791
< 0.1%
25691
< 0.1%
19791
< 0.1%

lib_grp3
Categorical

MISSING

Distinct9
Distinct (%)0.2%
Missing8913
Missing (%)69.9%
Memory size99.8 KiB
Tous les candidats sauf les Bac technologiques et Bac professionnels
1319 
Bacheliers professionnels toutes séries
1300 
Bacheliers technologiques toutes séries
1212 
Bac S SVT/EAT spécialité SVT
 
5
Bac S SVT/EAT spécialités Math/Physique/ISN/EAT
 
4
Other values (4)
 
7

Length

Max length68
Median length39
Mean length48.88536522
Min length5

Characters and Unicode

Total characters188062
Distinct characters34
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st rowBacheliers professionnels toutes séries
2nd rowBacheliers professionnels toutes séries
3rd rowBacheliers professionnels toutes séries
4th rowBacheliers professionnels toutes séries
5th rowBacheliers professionnels toutes séries
ValueCountFrequency (%)
Tous les candidats sauf les Bac technologiques et Bac professionnels1319
 
10.3%
Bacheliers professionnels toutes séries1300
 
10.2%
Bacheliers technologiques toutes séries1212
 
9.5%
Bac S SVT/EAT spécialité SVT5
 
< 0.1%
Bac S SVT/EAT spécialités Math/Physique/ISN/EAT4
 
< 0.1%
Bac S4
 
< 0.1%
Parcours particuliers1
 
< 0.1%
Tous les candidats1
 
< 0.1%
Bac STI2D - AEI1
 
< 0.1%
(Missing)8913
69.9%
2021-03-18T16:43:59.122516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-18T16:43:59.186938image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
bac2652
11.4%
les2639
11.3%
professionnels2619
11.2%
technologiques2531
10.9%
bacheliers2512
10.8%
séries2512
10.8%
toutes2512
10.8%
candidats1320
5.7%
tous1320
5.7%
et1319
5.7%
Other values (12)1364
5.9%

Most occurring characters

ValueCountFrequency (%)
s27053
14.4%
e24311
12.9%
19453
10.3%
o14133
 
7.5%
i11518
 
6.1%
l10311
 
5.5%
t10208
 
5.4%
a9138
 
4.9%
n9089
 
4.8%
c9026
 
4.8%
Other values (24)43822
23.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter161980
86.1%
Space Separator19453
 
10.3%
Uppercase Letter6606
 
3.5%
Other Punctuation21
 
< 0.1%
Decimal Number1
 
< 0.1%
Dash Punctuation1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
s27053
16.7%
e24311
15.0%
o14133
8.7%
i11518
 
7.1%
l10311
 
6.4%
t10208
 
6.3%
a9138
 
5.6%
n9089
 
5.6%
c9026
 
5.6%
u7688
 
4.7%
Other values (9)29505
18.2%
ValueCountFrequency (%)
B5164
78.2%
T1348
 
20.4%
S32
 
0.5%
V14
 
0.2%
E14
 
0.2%
A14
 
0.2%
I6
 
0.1%
P5
 
0.1%
M4
 
0.1%
N4
 
0.1%
ValueCountFrequency (%)
19453
100.0%
ValueCountFrequency (%)
/21
100.0%
ValueCountFrequency (%)
21
100.0%
ValueCountFrequency (%)
-1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin168586
89.6%
Common19476
 
10.4%

Most frequent character per script

ValueCountFrequency (%)
s27053
16.0%
e24311
14.4%
o14133
 
8.4%
i11518
 
6.8%
l10311
 
6.1%
t10208
 
6.1%
a9138
 
5.4%
n9089
 
5.4%
c9026
 
5.4%
u7688
 
4.6%
Other values (20)36111
21.4%
ValueCountFrequency (%)
19453
99.9%
/21
 
0.1%
21
 
< 0.1%
-1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII185532
98.7%
None2530
 
1.3%

Most frequent character per block

ValueCountFrequency (%)
s27053
14.6%
e24311
13.1%
19453
10.5%
o14133
 
7.6%
i11518
 
6.2%
l10311
 
5.6%
t10208
 
5.5%
a9138
 
4.9%
n9089
 
4.9%
c9026
 
4.9%
Other values (23)41292
22.3%
ValueCountFrequency (%)
é2530
100.0%

ran_grp3
Real number (ℝ≥0)

MISSING
SKEWED

Distinct224
Distinct (%)5.8%
Missing8913
Missing (%)69.9%
Infinite0
Infinite (%)0.0%
Mean54.00649857
Minimum1
Maximum10044
Zeros0
Zeros (%)0.0%
Memory size99.8 KiB
2021-03-18T16:43:59.305268image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q118.5
median36
Q362
95-th percentile123
Maximum10044
Range10043
Interquartile range (IQR)43.5

Descriptive statistics

Standard deviation235.8928651
Coefficient of variation (CV)4.367860745
Kurtosis1388.295601
Mean54.00649857
Median Absolute Deviation (MAD)20
Skewness35.5607139
Sum207763
Variance55645.4438
MonotocityNot monotonic
2021-03-18T16:43:59.412284image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1769
 
0.5%
2069
 
0.5%
1669
 
0.5%
2466
 
0.5%
2664
 
0.5%
1064
 
0.5%
2163
 
0.5%
2562
 
0.5%
2261
 
0.5%
1961
 
0.5%
Other values (214)3199
 
25.1%
(Missing)8913
69.9%
ValueCountFrequency (%)
141
0.3%
250
0.4%
349
0.4%
442
0.3%
553
0.4%
ValueCountFrequency (%)
100441
< 0.1%
89131
< 0.1%
45531
< 0.1%
16311
< 0.1%
15211
< 0.1%

lib_grp4
Categorical

MISSING

Distinct5
Distinct (%)35.7%
Missing12746
Missing (%)99.9%
Memory size99.8 KiB
Bac S
Bac S SVT/EAT spécialité SVT
Bac S Sciences de l'Ingénieur
Bac S SVT/EAT spécialités Math/Physique/ISN/EAT
Parcours particuliers

Length

Max length47
Median length28
Mean length23.85714286
Min length5

Characters and Unicode

Total characters334
Distinct characters31
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)7.1%

Sample

1st rowBac S Sciences de l'Ingénieur
2nd rowBac S SVT/EAT spécialité SVT
3rd rowBac S
4th rowBac S
5th rowBac S SVT/EAT spécialités Math/Physique/ISN/EAT
ValueCountFrequency (%)
Bac S4
 
< 0.1%
Bac S SVT/EAT spécialité SVT4
 
< 0.1%
Bac S Sciences de l'Ingénieur3
 
< 0.1%
Bac S SVT/EAT spécialités Math/Physique/ISN/EAT2
 
< 0.1%
Parcours particuliers1
 
< 0.1%
(Missing)12746
99.9%
2021-03-18T16:43:59.615109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-18T16:43:59.670038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
s13
23.6%
bac13
23.6%
svt/eat6
10.9%
spécialité4
 
7.3%
svt4
 
7.3%
l'ingénieur3
 
5.5%
de3
 
5.5%
sciences3
 
5.5%
spécialités2
 
3.6%
math/physique/isn/eat2
 
3.6%
Other values (2)2
 
3.6%

Most occurring characters

ValueCountFrequency (%)
41
 
12.3%
S28
 
8.4%
c27
 
8.1%
a23
 
6.9%
i22
 
6.6%
T18
 
5.4%
e15
 
4.5%
s15
 
4.5%
é15
 
4.5%
B13
 
3.9%
Other values (21)117
35.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter181
54.2%
Uppercase Letter97
29.0%
Space Separator41
 
12.3%
Other Punctuation15
 
4.5%

Most frequent character per category

ValueCountFrequency (%)
c27
14.9%
a23
12.7%
i22
12.2%
e15
8.3%
s15
8.3%
é15
8.3%
l10
 
5.5%
n9
 
5.0%
t9
 
5.0%
u7
 
3.9%
Other values (8)29
16.0%
ValueCountFrequency (%)
S28
28.9%
T18
18.6%
B13
13.4%
V10
 
10.3%
E8
 
8.2%
A8
 
8.2%
I5
 
5.2%
P3
 
3.1%
M2
 
2.1%
N2
 
2.1%
ValueCountFrequency (%)
/12
80.0%
'3
 
20.0%
ValueCountFrequency (%)
41
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin278
83.2%
Common56
 
16.8%

Most frequent character per script

ValueCountFrequency (%)
S28
 
10.1%
c27
 
9.7%
a23
 
8.3%
i22
 
7.9%
T18
 
6.5%
e15
 
5.4%
s15
 
5.4%
é15
 
5.4%
B13
 
4.7%
l10
 
3.6%
Other values (18)92
33.1%
ValueCountFrequency (%)
41
73.2%
/12
 
21.4%
'3
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII319
95.5%
None15
 
4.5%

Most frequent character per block

ValueCountFrequency (%)
41
 
12.9%
S28
 
8.8%
c27
 
8.5%
a23
 
7.2%
i22
 
6.9%
T18
 
5.6%
e15
 
4.7%
s15
 
4.7%
B13
 
4.1%
/12
 
3.8%
Other values (20)105
32.9%
ValueCountFrequency (%)
é15
100.0%

ran_grp4
Real number (ℝ≥0)

MISSING

Distinct14
Distinct (%)100.0%
Missing12746
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean1493.285714
Minimum89
Maximum9066
Zeros0
Zeros (%)0.0%
Memory size99.8 KiB
2021-03-18T16:43:59.766296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile100.7
Q1155.5
median348
Q31594.75
95-th percentile5729.55
Maximum9066
Range8977
Interquartile range (IQR)1439.25

Descriptive statistics

Standard deviation2485.56319
Coefficient of variation (CV)1.664492713
Kurtosis6.912053337
Mean1493.285714
Median Absolute Deviation (MAD)248
Skewness2.531207199
Sum20906
Variance6178024.374
MonotocityNot monotonic
2021-03-18T16:43:59.864921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1671
 
< 0.1%
1151
 
< 0.1%
6031
 
< 0.1%
29791
 
< 0.1%
4161
 
< 0.1%
1541
 
< 0.1%
39331
 
< 0.1%
90661
 
< 0.1%
2801
 
< 0.1%
17711
 
< 0.1%
Other values (4)4
 
< 0.1%
(Missing)12746
99.9%
ValueCountFrequency (%)
891
< 0.1%
1071
< 0.1%
1151
< 0.1%
1541
< 0.1%
1601
< 0.1%
ValueCountFrequency (%)
90661
< 0.1%
39331
< 0.1%
29791
< 0.1%
17711
< 0.1%
10661
< 0.1%

lib_grp5
Categorical

MISSING

Distinct5
Distinct (%)35.7%
Missing12746
Missing (%)99.9%
Memory size99.8 KiB
Bac S Sciences de l'Ingénieur
Bac S SVT/EAT spécialités Math/Physique/ISN/EAT
Parcours particuliers
Bac S SVT/EAT spécialité SVT
Bac S

Length

Max length47
Median length28.5
Mean length29.21428571
Min length5

Characters and Unicode

Total characters409
Distinct characters31
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)7.1%

Sample

1st rowParcours particuliers
2nd rowBac S Sciences de l'Ingénieur
3rd rowBac S Sciences de l'Ingénieur
4th rowBac S SVT/EAT spécialités Math/Physique/ISN/EAT
5th rowBac S SVT/EAT spécialité SVT
ValueCountFrequency (%)
Bac S Sciences de l'Ingénieur4
 
< 0.1%
Bac S SVT/EAT spécialités Math/Physique/ISN/EAT3
 
< 0.1%
Parcours particuliers3
 
< 0.1%
Bac S SVT/EAT spécialité SVT3
 
< 0.1%
Bac S1
 
< 0.1%
(Missing)12746
99.9%
2021-03-18T16:44:00.098322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-18T16:44:00.161578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
s11
19.0%
bac11
19.0%
svt/eat6
10.3%
l'ingénieur4
 
6.9%
de4
 
6.9%
sciences4
 
6.9%
particuliers3
 
5.2%
parcours3
 
5.2%
spécialité3
 
5.2%
spécialités3
 
5.2%
Other values (2)6
10.3%

Most occurring characters

ValueCountFrequency (%)
44
 
10.8%
c31
 
7.6%
i29
 
7.1%
S27
 
6.6%
a26
 
6.4%
s22
 
5.4%
e22
 
5.4%
T18
 
4.4%
r16
 
3.9%
é16
 
3.9%
Other values (21)158
38.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter244
59.7%
Uppercase Letter102
24.9%
Space Separator44
 
10.8%
Other Punctuation19
 
4.6%

Most frequent character per category

ValueCountFrequency (%)
c31
12.7%
i29
11.9%
a26
10.7%
s22
9.0%
e22
9.0%
r16
 
6.6%
é16
 
6.6%
u13
 
5.3%
l13
 
5.3%
t12
 
4.9%
Other values (8)44
18.0%
ValueCountFrequency (%)
S27
26.5%
T18
17.6%
B11
10.8%
V9
 
8.8%
E9
 
8.8%
A9
 
8.8%
I7
 
6.9%
P6
 
5.9%
M3
 
2.9%
N3
 
2.9%
ValueCountFrequency (%)
/15
78.9%
'4
 
21.1%
ValueCountFrequency (%)
44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin346
84.6%
Common63
 
15.4%

Most frequent character per script

ValueCountFrequency (%)
c31
 
9.0%
i29
 
8.4%
S27
 
7.8%
a26
 
7.5%
s22
 
6.4%
e22
 
6.4%
T18
 
5.2%
r16
 
4.6%
é16
 
4.6%
u13
 
3.8%
Other values (18)126
36.4%
ValueCountFrequency (%)
44
69.8%
/15
 
23.8%
'4
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII393
96.1%
None16
 
3.9%

Most frequent character per block

ValueCountFrequency (%)
44
 
11.2%
c31
 
7.9%
i29
 
7.4%
S27
 
6.9%
a26
 
6.6%
s22
 
5.6%
e22
 
5.6%
T18
 
4.6%
r16
 
4.1%
/15
 
3.8%
Other values (20)143
36.4%
ValueCountFrequency (%)
é16
100.0%

ran_grp5
Real number (ℝ≥0)

MISSING

Distinct14
Distinct (%)100.0%
Missing12746
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean1532
Minimum19
Maximum5040
Zeros0
Zeros (%)0.0%
Memory size99.8 KiB
2021-03-18T16:44:00.253446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile86.6
Q1187.25
median1111.5
Q32734.25
95-th percentile3881.7
Maximum5040
Range5021
Interquartile range (IQR)2547

Descriptive statistics

Standard deviation1553.879116
Coefficient of variation (CV)1.014281407
Kurtosis0.1594834292
Mean1532
Median Absolute Deviation (MAD)965.5
Skewness0.936876674
Sum21448
Variance2414540.308
MonotocityNot monotonic
2021-03-18T16:44:00.357733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
32581
 
< 0.1%
3351
 
< 0.1%
29961
 
< 0.1%
191
 
< 0.1%
1591
 
< 0.1%
19111
 
< 0.1%
29791
 
< 0.1%
20001
 
< 0.1%
50401
 
< 0.1%
11931
 
< 0.1%
Other values (4)4
 
< 0.1%
(Missing)12746
99.9%
ValueCountFrequency (%)
191
< 0.1%
1231
< 0.1%
1331
< 0.1%
1591
< 0.1%
2721
< 0.1%
ValueCountFrequency (%)
50401
< 0.1%
32581
< 0.1%
29961
< 0.1%
29791
< 0.1%
20001
< 0.1%

taux_adm_psup
Real number (ℝ≥0)

MISSING

Distinct100
Distinct (%)0.8%
Missing234
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean52.20269839
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Memory size99.8 KiB
2021-03-18T16:44:00.458924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q130
median47
Q374
95-th percentile100
Maximum100
Range99
Interquartile range (IQR)44

Descriptive statistics

Standard deviation27.84279844
Coefficient of variation (CV)0.5333593722
Kurtosis-1.027605123
Mean52.20269839
Median Absolute Deviation (MAD)21
Skewness0.3315100039
Sum653891
Variance775.2214251
MonotocityNot monotonic
2021-03-18T16:44:00.568358image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1001016
 
8.0%
99290
 
2.3%
32216
 
1.7%
24205
 
1.6%
36197
 
1.5%
34191
 
1.5%
33187
 
1.5%
43187
 
1.5%
38186
 
1.5%
29183
 
1.4%
Other values (90)9668
75.8%
(Missing)234
 
1.8%
ValueCountFrequency (%)
12
 
< 0.1%
222
0.2%
332
0.3%
427
0.2%
527
0.2%
ValueCountFrequency (%)
1001016
8.0%
99290
 
2.3%
98133
 
1.0%
9793
 
0.7%
9688
 
0.7%

taux_adm_psup_pro
Real number (ℝ≥0)

MISSING
ZEROS

Distinct97
Distinct (%)0.8%
Missing1062
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean21.40630877
Minimum0
Maximum100
Zeros3305
Zeros (%)25.9%
Memory size99.8 KiB
2021-03-18T16:44:00.676035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median13
Q338
95-th percentile67
Maximum100
Range100
Interquartile range (IQR)38

Descriptive statistics

Standard deviation23.5740786
Coefficient of variation (CV)1.101267801
Kurtosis0.7189504872
Mean21.40630877
Median Absolute Deviation (MAD)13
Skewness1.122815769
Sum250411
Variance555.7371817
MonotocityNot monotonic
2021-03-18T16:44:00.787776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03305
25.9%
4273
 
2.1%
7249
 
2.0%
3245
 
1.9%
13234
 
1.8%
1224
 
1.8%
50217
 
1.7%
2213
 
1.7%
6210
 
1.6%
33197
 
1.5%
Other values (87)6331
49.6%
(Missing)1062
 
8.3%
ValueCountFrequency (%)
03305
25.9%
1224
 
1.8%
2213
 
1.7%
3245
 
1.9%
4273
 
2.1%
ValueCountFrequency (%)
100160
1.3%
962
 
< 0.1%
941
 
< 0.1%
937
 
0.1%
9213
 
0.1%

taux_adm_psup_gen
Real number (ℝ≥0)

MISSING
ZEROS

Distinct100
Distinct (%)0.9%
Missing1062
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean50.23089417
Minimum0
Maximum100
Zeros762
Zeros (%)6.0%
Memory size99.8 KiB
2021-03-18T16:44:00.909528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118
median50
Q380
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)62

Descriptive statistics

Standard deviation33.76362694
Coefficient of variation (CV)0.6721685429
Kurtosis-1.3909574
Mean50.23089417
Median Absolute Deviation (MAD)31
Skewness0.06690431197
Sum587601
Variance1139.982504
MonotocityNot monotonic
2021-03-18T16:44:01.027244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1001384
 
10.8%
0762
 
6.0%
13234
 
1.8%
9206
 
1.6%
17189
 
1.5%
7188
 
1.5%
50175
 
1.4%
8171
 
1.3%
33163
 
1.3%
20161
 
1.3%
Other values (90)8065
63.2%
(Missing)1062
 
8.3%
ValueCountFrequency (%)
0762
6.0%
29
 
0.1%
352
 
0.4%
4114
 
0.9%
573
 
0.6%
ValueCountFrequency (%)
1001384
10.8%
9960
 
0.5%
9877
 
0.6%
9794
 
0.7%
9684
 
0.7%

taux_adm_psup_techno
Real number (ℝ≥0)

MISSING
ZEROS

Distinct93
Distinct (%)0.8%
Missing1062
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean27.56317319
Minimum0
Maximum100
Zeros1788
Zeros (%)14.0%
Memory size99.8 KiB
2021-03-18T16:44:01.173129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median27
Q342
95-th percentile62
Maximum100
Range100
Interquartile range (IQR)32

Descriptive statistics

Standard deviation21.09444927
Coefficient of variation (CV)0.7653128006
Kurtosis0.6499772325
Mean27.56317319
Median Absolute Deviation (MAD)16
Skewness0.6982320259
Sum322434
Variance444.9757899
MonotocityNot monotonic
2021-03-18T16:44:01.320586image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01788
 
14.0%
33336
 
2.6%
50290
 
2.3%
38277
 
2.2%
25251
 
2.0%
40224
 
1.8%
13220
 
1.7%
43216
 
1.7%
20215
 
1.7%
29214
 
1.7%
Other values (83)7667
60.1%
(Missing)1062
 
8.3%
ValueCountFrequency (%)
01788
14.0%
162
 
0.5%
273
 
0.6%
3122
 
1.0%
4114
 
0.9%
ValueCountFrequency (%)
100177
1.4%
971
 
< 0.1%
941
 
< 0.1%
932
 
< 0.1%
901
 
< 0.1%

tri
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.8 KiB
2_Lycées
6020 
1_universités
4066 
3_Autres formations
2674 

Length

Max length19
Median length13
Mean length11.8984326
Min length8

Characters and Unicode

Total characters151824
Distinct characters22
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3_Autres formations
2nd row3_Autres formations
3rd row3_Autres formations
4th row3_Autres formations
5th row3_Autres formations
ValueCountFrequency (%)
2_Lycées6020
47.2%
1_universités4066
31.9%
3_Autres formations2674
21.0%
2021-03-18T16:44:01.515501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-18T16:44:01.578110image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2_lycées6020
39.0%
1_universités4066
26.3%
3_autres2674
17.3%
formations2674
17.3%

Most occurring characters

ValueCountFrequency (%)
s19500
12.8%
_12760
 
8.4%
e12760
 
8.4%
i10806
 
7.1%
é10086
 
6.6%
t9414
 
6.2%
r9414
 
6.2%
u6740
 
4.4%
n6740
 
4.4%
26020
 
4.0%
Other values (12)47584
31.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter114936
75.7%
Decimal Number12760
 
8.4%
Connector Punctuation12760
 
8.4%
Uppercase Letter8694
 
5.7%
Space Separator2674
 
1.8%

Most frequent character per category

ValueCountFrequency (%)
s19500
17.0%
e12760
11.1%
i10806
9.4%
é10086
8.8%
t9414
8.2%
r9414
8.2%
u6740
 
5.9%
n6740
 
5.9%
y6020
 
5.2%
c6020
 
5.2%
Other values (5)17436
15.2%
ValueCountFrequency (%)
26020
47.2%
14066
31.9%
32674
21.0%
ValueCountFrequency (%)
L6020
69.2%
A2674
30.8%
ValueCountFrequency (%)
_12760
100.0%
ValueCountFrequency (%)
2674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin123630
81.4%
Common28194
 
18.6%

Most frequent character per script

ValueCountFrequency (%)
s19500
15.8%
e12760
10.3%
i10806
8.7%
é10086
 
8.2%
t9414
 
7.6%
r9414
 
7.6%
u6740
 
5.5%
n6740
 
5.5%
L6020
 
4.9%
y6020
 
4.9%
Other values (7)26130
21.1%
ValueCountFrequency (%)
_12760
45.3%
26020
21.4%
14066
 
14.4%
32674
 
9.5%
2674
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII141738
93.4%
None10086
 
6.6%

Most frequent character per block

ValueCountFrequency (%)
s19500
13.8%
_12760
 
9.0%
e12760
 
9.0%
i10806
 
7.6%
t9414
 
6.6%
r9414
 
6.6%
u6740
 
4.8%
n6740
 
4.8%
26020
 
4.2%
L6020
 
4.2%
Other values (11)41564
29.3%
ValueCountFrequency (%)
é10086
100.0%

cod_aff_form
Real number (ℝ≥0)

UNIQUE

Distinct12760
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13411.28182
Minimum2
Maximum30705
Zeros0
Zeros (%)0.0%
Memory size99.8 KiB
2021-03-18T16:44:01.663606image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2616.95
Q16180.75
median10508.5
Q322100
95-th percentile28431.05
Maximum30705
Range30703
Interquartile range (IQR)15919.25

Descriptive statistics

Standard deviation8706.323598
Coefficient of variation (CV)0.6491790804
Kurtosis-1.188837798
Mean13411.28182
Median Absolute Deviation (MAD)6111
Skewness0.4639946401
Sum171127956
Variance75800070.6
MonotocityNot monotonic
2021-03-18T16:44:01.766887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20471
 
< 0.1%
231981
 
< 0.1%
68061
 
< 0.1%
27081
 
< 0.1%
88491
 
< 0.1%
231821
 
< 0.1%
272721
 
< 0.1%
47431
 
< 0.1%
67901
 
< 0.1%
149781
 
< 0.1%
Other values (12750)12750
99.9%
ValueCountFrequency (%)
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
ValueCountFrequency (%)
307051
< 0.1%
307011
< 0.1%
307001
< 0.1%
306991
< 0.1%
306971
< 0.1%

Missing values

2021-03-18T16:43:25.176692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-03-18T16:43:29.820736image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-03-18T16:43:31.561243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-03-18T16:43:32.906402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

sessioncontrat_etabcod_uaig_ea_lib_vxdepdep_libregion_etab_affacad_miesselect_formfililib_comp_voe_insform_lib_voe_accregr_formafil_lib_voe_accdetail_formalien_form_psupg_olocalisation_des_formationscapa_finvoe_totvoe_tot_fnb_voe_ppnb_voe_pp_internatnb_voe_pp_bgnb_voe_pp_bg_brsnb_voe_pp_btnb_voe_pp_bt_brsnb_voe_pp_bpnb_voe_pp_bp_brsnb_voe_pp_atnb_voe_pcnb_voe_pc_bgnb_voe_pc_btnb_voe_pc_bpnb_voe_pc_atnb_cla_ppnb_cla_pcnb_cla_pp_internatnb_cla_pp_pasinternatnb_cla_pp_bgnb_cla_pp_bg_brsnb_cla_pp_btnb_cla_pp_bt_brsnb_cla_pp_bpnb_cla_pp_bp_brsnb_cla_pp_atprop_totacc_totacc_tot_facc_ppacc_pcacc_debutppacc_datebacacc_finppacc_internatacc_brsacc_neobacacc_bgacc_btacc_bpacc_atacc_mention_nonrenseigneeacc_sansmentionacc_abacc_bacc_tbacc_bg_mentionacc_bt_mentionacc_bp_mentionacc_termacc_term_facc_aca_origacc_aca_orig_idfpct_acc_debutpppct_acc_datebacpct_acc_finpppct_fpct_aca_origpct_aca_orig_idfpct_etab_origpct_bourspct_neobacpct_mention_nonrenseigneepct_sansmentionpct_abpct_bpct_tbpct_bgpct_bg_mentionpct_btpct_bt_mentionpct_bppct_bp_mentionprop_tot_bgprop_tot_bg_brsprop_tot_btprop_tot_bt_brsprop_tot_bpprop_tot_bp_brsprop_tot_atlib_grp1ran_grp1lib_grp2ran_grp2lib_grp3ran_grp3lib_grp4ran_grp4lib_grp5ran_grp5taux_adm_psuptaux_adm_psup_protaux_adm_psup_gentaux_adm_psup_technotricod_aff_form
02020Public0754902WIFSI Virginie Olivier CH Sainte Anne75ParisIle-de-FranceParisformation selectiveIFSIRegroupement d'IFSI Université Paris Descartes (P5) - D.E InfirmierD.E secteur sanitaireNaND.E InfirmierNaNhttps://dossier.parcoursup.fr/Candidat/carte?ACTION=2&g_ti_cod=25148&g_ta_cod=2320748.8297,2.33989115.0471142304711NaN4328078528977936127150000040550NaNNaN4328074126565430222289431139811304.072.084.0NaN17432711570016161011494NaNNaN5373.5463.7274.3486.7311.6386.05NaN39.5338.050.037.20930237.20930223.2558142.32558162.79069832.5625.58139520.9311.6279079.30250.055.067.043.036.027.0590.0Tous les candidats1439.0NaNNaNNaNNaNNaNNaNNaNNaN24.012.072.016.03_Autres formations23207
12020Public0942070PIFSI Jean Baptiste Pussin94Val-de-MarneIle-de-FranceCréteilformation selectiveIFSIRegroupement d'IFSI Université Paris Descartes (P5) - D.E InfirmierD.E secteur sanitaireNaND.E InfirmierNaNhttps://dossier.parcoursup.fr/Candidat/carte?ACTION=2&g_ti_cod=25148&g_ta_cod=2320948.81977,2.4297681.0315728353157NaN2164842717855027019640000026470NaNNaN21648402163449217158061581728103.045.059.0NaN624154557010941635NaNNaN16193.7055.5672.8488.8966.6779.17NaN25.0029.630.041.66666737.50000016.6666674.16666762.50000025.0016.66666712.5020.83333320.83140.038.045.030.023.019.0407.0Tous les candidats1727.0NaNNaNNaNNaNNaNNaNNaNNaN25.010.080.09.03_Autres formations23209
22020Public0161051FIFSI - Croix-Rouge Française - Angoulême16CharenteNouvelle-AquitainePoitiersformation selectiveIFSIRegroupement d'IFSI Université Poitiers - D.E InfirmierD.E secteur sanitaireNaND.E InfirmierNaNhttps://dossier.parcoursup.fr/Candidat/carte?ACTION=2&g_ti_cod=22988&g_ta_cod=2321045.6181,0.1137120.0316326933163NaN610865891352659316990000020170NaNNaN487623606915642101491812410912408.088.0102.0NaN195418171970010151910101618NaNNaN33336.4570.9782.2687.9061.1161.11NaN35.1943.550.018.51851927.77777835.18518518.51851933.33333318.5231.48148129.6335.18518533.33238.034.0165.044.075.030.0440.0Tous les candidats1945.0NaNNaNNaNNaNNaNNaNNaNNaN36.023.057.019.03_Autres formations23210
32020Public0421574HIFSI St Chamond42LoireAuvergne-Rhône-AlpesLyonformation selectiveIFSIRegroupement d'IFSI Université St Etienne - D.E InfirmierD.E secteur sanitaireNaND.E InfirmierNaNhttps://dossier.parcoursup.fr/Candidat/carte?ACTION=2&g_ti_cod=22993&g_ta_cod=2323345.4691,4.5095452.0305826523058NaN497906282092629016710000021710NaNNaN4166251516112231111841552455200.043.045.0NaN82711124250414459104NaNNaN25250.0082.6986.5486.5492.5992.59NaN29.6351.920.014.81481551.85185214.81481518.51851940.74074133.3344.44444437.0414.81481514.8188.011.0161.046.09.02.0157.0Tous les candidats864.0NaNNaNNaNNaNNaNNaNNaNNaN20.08.046.046.03_Autres formations23233
42020Public0090559JIFSI CH Int Val Ariège09AriègeOccitanieToulouseformation selectiveIFSIRegroupement d'IFSI Université Toulouse - D.E InfirmierD.E secteur sanitaireNaND.E InfirmierNaNhttps://dossier.parcoursup.fr/Candidat/carte?ACTION=2&g_ti_cod=22985&g_ta_cod=2324743.1127,1.6085861.0505042545050NaN72112887824753019329210000039270NaNNaN62797715180433146215273361576100.026.041.0NaN8395191522001620351915NaNNaN17170.0042.6267.2193.4443.5943.59NaN20.5163.930.00.00000041.02564151.2820517.69230812.82051312.8248.71794948.7238.46153838.46134.015.0232.045.0163.050.0204.0Tous les candidats2360.0NaNNaNNaNNaNNaNNaNNaNNaN23.033.025.041.03_Autres formations23247
52020Public0810974UIFSI Albi81TarnOccitanieToulouseformation selectiveIFSIRegroupement d'IFSI Université Toulouse - D.E InfirmierD.E secteur sanitaireNaND.E InfirmierNaNhttps://dossier.parcoursup.fr/Candidat/carte?ACTION=2&g_ti_cod=22985&g_ta_cod=2325543.9183,2.13654106.0679658166796NaN1111192121334670925337630000053050NaNNaN98215398025356818927758621069610606.076.095.0NaN24852827302102324110262730NaNNaN68685.6671.7089.6290.5780.0080.00NaN28.2480.190.02.35294137.64705948.23529411.76470632.94117630.5931.76470631.7635.29411835.29206.019.0282.064.0175.055.0199.0Tous les candidats2016.0NaNNaNNaNNaNNaNNaNNaNNaN19.032.040.028.03_Autres formations23255
62020Public0371196ZIFSI du CHU de TOURS37Indre-et-LoireCentre-Val de LoireOrléans-Toursformation selectiveIFSIRegroupement d'IFSI Université Tours - D.E InfirmierD.E secteur sanitaireNaND.E InfirmierNaNhttps://dossier.parcoursup.fr/Candidat/carte?ACTION=2&g_ti_cod=22995&g_ta_cod=2326547.3471,0.71604158.0527045685270NaN1011184118228245814126190000047190NaNNaN95616611212633681122274791158147158015.0101.0129.0NaN2310832724500848391327694NaNNaN40409.4963.9281.6593.0437.0437.04NaN21.3068.350.07.40740744.44444436.11111112.03703729.62963025.0066.66666763.893.7037043.70173.021.0328.065.030.011.0260.0Tous les candidats1464.0NaNNaNNaNNaNNaNNaNNaNNaN23.04.035.061.03_Autres formations23265
72020Public0410820UIFSI CH Blois41Loir-et-CherCentre-Val de LoireOrléans-Toursformation selectiveIFSIRegroupement d'IFSI Université Tours - D.E InfirmierD.E secteur sanitaireNaND.E InfirmierNaNhttps://dossier.parcoursup.fr/Candidat/carte?ACTION=2&g_ti_cod=22995&g_ta_cod=2326847.5858,1.32677107.0393234013932NaN6871209031983389520040000035210NaNNaN65011086018228176173010911079910704.064.083.0NaN11761647133101930252103413NaNNaN49493.7459.8177.5792.5264.4764.47NaN14.4771.030.025.00000039.47368432.8947372.63157921.05263213.1661.84210544.7417.10526317.11187.028.0409.075.058.020.0437.0Tous les candidats2587.0NaNNaNNaNNaNNaNNaNNaNNaN41.014.031.055.03_Autres formations23268
82020Public0451147CIFSI CHR Orléans45LoiretCentre-Val de LoireOrléans-Toursformation selectiveIFSIRegroupement d'IFSI Université Tours - D.E InfirmierD.E secteur sanitaireNaND.E InfirmierNaNhttps://dossier.parcoursup.fr/Candidat/carte?ACTION=2&g_ti_cod=22995&g_ta_cod=2327047.9079,1.88604149.0471041024710NaN861171105724844313923490000041470NaNNaN8061549962293491041996118215514115508.085.0125.0NaN26872550126801632309194012NaNNaN60605.1654.8480.6590.9768.9768.97NaN29.8956.130.018.39080536.78160934.48275910.34482828.73563221.8457.47126445.9813.79310313.79238.041.0441.091.052.019.0451.0Tous les candidats2539.0NaNNaNNaNNaNNaNNaNNaNNaN39.010.036.054.03_Autres formations23270
92020Public0771989UIFSI du CH de Provins77Seine-et-MarneIle-de-FranceCréteilformation selectiveIFSIRegroupement d'IFSI UPEC (P12) - D.E InfirmierD.E secteur sanitaireNaND.E InfirmierNaNhttps://dossier.parcoursup.fr/Candidat/carte?ACTION=2&g_ti_cod=23006&g_ta_cod=2327648.5601,3.299244.0202117992021NaN237613431022099212320000016310NaNNaN23059266741476698843643394302.026.039.0NaN52811125150811814115NaNNaN16164.6560.4790.7090.7057.1457.14NaN17.8665.120.028.57142939.28571428.5714293.57142939.28571414.2942.85714339.2917.85714317.8690.013.095.020.044.017.0207.0Tous les candidats1900.0NaNNaNNaNNaNNaNNaNNaNNaN32.016.047.037.03_Autres formations23276

Last rows

sessioncontrat_etabcod_uaig_ea_lib_vxdepdep_libregion_etab_affacad_miesselect_formfililib_comp_voe_insform_lib_voe_accregr_formafil_lib_voe_accdetail_formalien_form_psupg_olocalisation_des_formationscapa_finvoe_totvoe_tot_fnb_voe_ppnb_voe_pp_internatnb_voe_pp_bgnb_voe_pp_bg_brsnb_voe_pp_btnb_voe_pp_bt_brsnb_voe_pp_bpnb_voe_pp_bp_brsnb_voe_pp_atnb_voe_pcnb_voe_pc_bgnb_voe_pc_btnb_voe_pc_bpnb_voe_pc_atnb_cla_ppnb_cla_pcnb_cla_pp_internatnb_cla_pp_pasinternatnb_cla_pp_bgnb_cla_pp_bg_brsnb_cla_pp_btnb_cla_pp_bt_brsnb_cla_pp_bpnb_cla_pp_bp_brsnb_cla_pp_atprop_totacc_totacc_tot_facc_ppacc_pcacc_debutppacc_datebacacc_finppacc_internatacc_brsacc_neobacacc_bgacc_btacc_bpacc_atacc_mention_nonrenseigneeacc_sansmentionacc_abacc_bacc_tbacc_bg_mentionacc_bt_mentionacc_bp_mentionacc_termacc_term_facc_aca_origacc_aca_orig_idfpct_acc_debutpppct_acc_datebacpct_acc_finpppct_fpct_aca_origpct_aca_orig_idfpct_etab_origpct_bourspct_neobacpct_mention_nonrenseigneepct_sansmentionpct_abpct_bpct_tbpct_bgpct_bg_mentionpct_btpct_bt_mentionpct_bppct_bp_mentionprop_tot_bgprop_tot_bg_brsprop_tot_btprop_tot_bt_brsprop_tot_bpprop_tot_bp_brsprop_tot_atlib_grp1ran_grp1lib_grp2ran_grp2lib_grp3ran_grp3lib_grp4ran_grp4lib_grp5ran_grp5taux_adm_psuptaux_adm_psup_protaux_adm_psup_gentaux_adm_psup_technotricod_aff_form
127502020Public0692987XIFSI CH Le Vinatier69RhôneAuvergne-Rhône-AlpesLyonformation selectiveIFSIRegroupement d'IFSI Université Lyon - D.E InfirmierD.E secteur sanitaireNaND.E InfirmierNaNhttps://dossier.parcoursup.fr/Candidat/carte?ACTION=2&g_ti_cod=23001&g_ta_cod=2316945.7396,4.893496.0875775868757NaN1369290154750579727950440000046730NaNNaN941143949253440141234388696899609.062.079.0NaN1352152017440216277132017NaNNaN29299.3864.5882.2992.7155.7755.77NaN25.0054.170.03.84615430.76923151.92307713.46153828.84615425.0038.46153838.4632.69230832.69197.019.0225.049.0142.035.0322.0Tous les candidats2308.0NaNNaNNaNNaNNaNNaNNaNNaN18.025.045.030.03_Autres formations23169
127512020Public0110816CIFSI CH Narbonne11AudeOccitanieMontpellierformation selectiveIFSIRegroupement d'IFSI Université Montpellier - D.E InfirmierD.E secteur sanitaireNaND.E InfirmierNaNhttps://dossier.parcoursup.fr/Candidat/carte?ACTION=2&g_ti_cod=22991&g_ta_cod=2317243.1838,3.0020368.0720461667204NaN1053218133240273127140880000064760NaNNaN10262031180332605211366572867546701.026.038.0NaN16401913827041711816128NaNNaN32321.4938.8156.7280.6080.0080.00NaN40.0059.700.010.00000042.50000027.50000020.00000047.50000040.0032.50000030.0020.00000020.00192.031.098.028.026.015.0412.0Tous les candidats2007.0NaNNaNNaNNaNNaNNaNNaNNaN17.013.057.030.03_Autres formations23172
127522020Public0341334RIFSI CHU Montpellier34HéraultOccitanieMontpellierformation selectiveIFSIRegroupement d'IFSI Université Montpellier - D.E InfirmierD.E secteur sanitaireNaND.E InfirmierNaNhttps://dossier.parcoursup.fr/Candidat/carte?ACTION=2&g_ti_cod=22991&g_ta_cod=2317743.6235,3.85783134.010149874210149NaN17304231872613108742754600000090530NaNNaN167239216274948843304870597134115134012.074.095.0NaN197037276640317292134276NaNNaN54548.9655.2270.9085.8277.1477.14NaN27.1452.240.04.28571424.28571441.42857130.00000052.85714348.5738.57142938.578.5714298.57207.045.078.027.021.07.0291.0Tous les candidats789.0NaNNaNNaNNaNNaNNaNNaNNaN7.04.069.025.03_Autres formations23177
127532020Public0542526CIFSI Lionnois54Meurthe-et-MoselleGrand EstNancy-Metzformation selectiveIFSIRegroupement d'IFSI Université Nancy - D.E InfirmierD.E secteur sanitaireNaND.E InfirmierNaNhttps://dossier.parcoursup.fr/Candidat/carte?ACTION=2&g_ti_cod=22997&g_ta_cod=2318548.684,6.19258132.0399233323992NaN90021675822541515619190000033000NaNNaN8501935971593211111532649140127140012.093.0126.0NaN178959264510253027735254NaNNaN74748.5766.4390.0090.7183.1583.15NaN19.1063.570.028.08988833.70786530.3370797.86516966.29213539.3329.21348328.094.4943824.49241.036.0119.035.034.011.0255.0Tous les candidats1530.0NaNNaNNaNNaNNaNNaNNaNNaN26.04.074.022.03_Autres formations23185
127542020Public0542018AIFSI Laxou54Meurthe-et-MoselleGrand EstNancy-Metzformation selectiveIFSIRegroupement d'IFSI Université Nancy - D.E InfirmierD.E secteur sanitaireNaND.E InfirmierNaNhttps://dossier.parcoursup.fr/Candidat/carte?ACTION=2&g_ti_cod=22997&g_ta_cod=2318648.6775,6.1362865.0364630413646NaN79018167718838513817940000030340NaNNaN749161545135301991439337726172010.060.068.0NaN14452711727041320823117NaNNaN444413.8983.3394.4484.7297.7897.78NaN31.1162.500.08.88888928.88888944.44444417.77777860.00000051.1124.44444424.4415.55555615.56125.025.058.020.017.07.0137.0Tous les candidats1089.0NaNNaNNaNNaNNaNNaNNaNNaN19.013.068.019.03_Autres formations23186
127552020Privé sous contrat d'association0572589EIFSI Croix Rouge Française Metz57MoselleGrand EstNancy-Metzformation selectiveIFSIRegroupement d'IFSI Université Nancy - D.E InfirmierD.E secteur sanitaireNaND.E InfirmierNaNhttps://dossier.parcoursup.fr/Candidat/carte?ACTION=2&g_ti_cod=22997&g_ta_cod=2318949.1124,6.2075192.0369830773698NaN74116669920443216218260000030110NaNNaN694147556145334118142789394879402.044.074.0NaN16602515203401132161151420NaNNaN53532.1346.8178.7292.5588.3388.33NaN26.6763.830.018.33333353.33333326.6666671.66666741.66666725.0025.00000023.3333.33333333.33293.049.0177.046.086.029.0337.0Tous les candidats2073.0NaNNaNNaNNaNNaNNaNNaNNaN34.027.050.023.03_Autres formations23189
127562020Public0572694UIFSI Sarreguemines57MoselleGrand EstNancy-Metzformation selectiveIFSIRegroupement d'IFSI Université Nancy - D.E InfirmierD.E secteur sanitaireNaND.E InfirmierNaNhttps://dossier.parcoursup.fr/Candidat/carte?ACTION=2&g_ti_cod=22997&g_ta_cod=2319049.10402,7.0616860.0274522342745NaN49712754316930511114000000022280NaNNaN46510843712024379108358064536403.040.054.0NaN1149171715150714199121515NaNNaN47474.6962.5084.3882.8195.9295.92NaN22.4576.560.014.28571428.57142938.77551018.36734734.69387824.4934.69387830.6130.61224530.61169.029.0124.032.067.015.0220.0Tous les candidats2305.0NaNNaNNaNNaNNaNNaNNaNNaN38.027.034.039.03_Autres formations23190
127572020Public0881509MIFSI Remiremont88VosgesGrand EstNancy-Metzformation selectiveIFSIRegroupement d'IFSI Université Nancy - D.E InfirmierD.E secteur sanitaireNaND.E InfirmierNaNhttps://dossier.parcoursup.fr/Candidat/carte?ACTION=2&g_ti_cod=22997&g_ta_cod=2319648.0141,6.5918143.0235419472354NaN392864761272418712450000019540NaNNaN374753971011926499146243384302.017.035.0NaN430121351305121127135NaNNaN13134.6539.5381.4088.3743.3343.33NaN13.3369.770.016.66666740.00000036.6666676.66666740.00000023.3343.33333343.3316.66666716.67122.021.0107.018.038.012.0195.0Tous les candidats2354.0NaNNaNNaNNaNNaNNaNNaNNaN41.019.040.042.03_Autres formations23196
127582020Public0881511PIFSI CH St Dié des Vosges88VosgesGrand EstNancy-Metzformation selectiveIFSIRegroupement d'IFSI Université Nancy - D.E InfirmierD.E secteur sanitaireNaND.E InfirmierNaNhttps://dossier.parcoursup.fr/Candidat/carte?ACTION=2&g_ti_cod=22997&g_ta_cod=2319848.2704,6.9459930.0249820612498NaN443955041472619612900000020840NaNNaN4238442511920367103335631273100.019.029.0NaN61983812016102738NaNNaN13130.0061.2993.5587.1068.4268.42NaN31.5861.290.05.26315831.57894752.63157910.52631642.10526336.8415.78947415.7942.10526342.11101.016.069.016.039.012.0147.0Tous les candidats1965.0NaNNaNNaNNaNNaNNaNNaNNaN35.032.055.013.03_Autres formations23198
127592020Privé sous contrat d'association0442569DIFSI CROIX ROUGE FRANCAISE Rezé44Loire-AtlantiquePays de la LoireNantesformation selectiveIFSIRegroupement d'IFSI Université Nantes - D.E InfirmierD.E secteur sanitaireNaND.E InfirmierNaNhttps://dossier.parcoursup.fr/Candidat/carte?ACTION=2&g_ti_cod=22994&g_ta_cod=2320247.1931,-1.5492674.0407935514079NaN76410077716444211520960000037200NaNNaN7489767213038796191362675647501.043.053.0NaN752231514230617218171514NaNNaN44441.3357.3370.6785.3384.6284.62NaN13.4669.330.011.53846232.69230840.38461515.38461544.23076932.6928.84615428.8526.92307726.92223.028.061.016.074.014.0268.0Tous les candidats1510.0NaNNaNNaNNaNNaNNaNNaNNaN23.019.057.024.03_Autres formations23202